This study focused on the research hotspots and development trends of the neuroimaging of attention deficit hyperactivity disorder (ADHD) in the past thirty years.The Web of Science database was searched for articles about ADHD neuroimaging from January 1992 to September 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals.2,621 articles were included. More and more articles have been published every year in the last years. These articles mainly come from 435 institutions and 65 countries/regions led by the United States. King's College London had the highest number of publications. The study identified 634 authors, among which Buitelaar, J. K. published the largest number of articles and Castellanos, F. X. was co-cited most often. The most productive and cited journal was Biological psychiatry. In recent years, burst keywords were resting-state fMRI, machine learning, functional connectivity, and networks. And a timeline chart of the cluster of keywords showed that “children” had the longest time span.Increased attention has been paid to ADHD neuroimaging. This work might assist researchers to identify new insight on potential collaborators and cooperative institutions, hot topics, and research directions. Attention-deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder among children. The worldwide prevalence has been estimated between 2 and 7%, with an average of approximately 5% , functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET), single-positron emission computed tomography (SPECT), and magnetic resonance spectroscopy (MRS), have revealed abnormalities in brain function in patients with ADHD. fMRI is a non-invasive, high spatial resolution technology detecting functional changes in the brain through changes in cerebral blood oxygen levels. The complex pathophysiology of ADHD has been further investigated by resting-state MRI (rs-fMRI) and task-state MRI (task-MRI) functional magnetic resonance imaging studies . The literature search was constrained to articles and review articles, eliminating other document types.This study was conducted by searching the Science Citation Index Expanded (SCI-Expanded) in the Web of Science (WoS). The relevant literature was published between 1 January 1992 and 20 September 2022, and the search subject terms were identified as (ADHD OR “Attention Deficit Disorder with Hyperactivity” OR “Hyperkinetic Syndrome” OR “hyperkinetic disorder” OR “Attention Deficit Hyperactivity Disorder”) AND , and a polynomial regression model was constructed, aiming to predict the number of articles in 2022. The data from the WoS were exported in “Full Record with Cited References” format and saved with the prefix “download.” “Node Types” select “Author,” “Institution,” “country,” and “Keyword” for visual analysis to generate a visual co-occurrence map. “Pruning” selects “Pathfinder” and “Pruning sliced networks” to prune each slice of the network to reduce the density of the network and improve its readability.k = 15), and pruning (pathfinder and pruning sliced network). Scimago Graphica 1.0.25 visualized the collaborative relationships of authors from different countries. In the visual diagram, each node was deemed as an element, and its size referred to the frequency of occurrence. Cooperation or connection among various elements was marked with a connection line. The node with high betweenness centrality (>0.1) is the key point in this field, which is also a concern of CiteSpace. The purple ring represents centrality. Highly cited elements are often considered to be the research hotspots in this field. The flow diagram of this study is shown in CiteSpace was employed for constructing a base map for hotspots and knowledge in nephrotoxicity research, and for centrality detection , England , and Germany  published the most articles. The United States contributed almost half of all publications, with the first article published in 1993 which was earlier than any other country, showing great contribution in volume and importance. Maps drawn after the national visual analysis include 65 countries/regions and 326 links. As shown in n = 168, 19.2%), Harvard University , Radboud univ Nijmegen , National Institute of Mental Health , and Yale University . It is worth noting that all these institutions are located in developed countries. The National Institute of Mental Health (NIMH) has a greater centrality (0.18). NIMH published the first article in 1993 about the child and adolescent psychopathology research , Rubia, Katya , Franke, Barbara , Castellanos, Francisco Xavier , and Hoekstra, Pieter J  had the highest citation counts, followed by Rubia K (n = 775), Barkley RA (n = 581), Shaw P (n = 532), and Biederman J , it is deemed as a knowledge basis of the research frontiers 's brain program, which was launched in the United States in 2013, has significantly increased funding and promoted ADHD neuroimaging research. The American periodical Biological Psychiatry (IF = 12.81) also has the most articles and citations. It is noteworthy that China, which ranks fourth in terms of the number of papers issued, is the only developing country in the top five. Research of the United Kingdom, Germany, and the Netherlands possess a central and significant position. Currently, most studies are conducted in developed countries such as Europe and the United States. Some scholars proposed that further research from less developed countries is needed to understand the interactions of neurobiological and socio-cultural elements in ADHD  at King's College London is the premier for neurosciences research in Europe. Its cited publications about mental health were the most (the first 1%) . However, an analysis of these institutions showed that articles on ADHD were mostly published by research universities, including a small number of hospitals. ADHD will evolve toward precise clinical phenotypes in the next few years. Thus, relevant research units should establish cooperation with hospitals with accurate diagnostic capabilities as increasing clinicians publish literature on ADHD.In this study, we learn more about the outstanding authors who contribute greatly to studies in this field. Author information can clearly identify the author's contribution to the field and establish potential partnerships. Buitelaar J, Rubia K, and Franke B contribute the greatest number of publications in the field of ADHD neuroimaging. Buitelaar J comes from the University of Bergen, and his research interests include ADHD, autism, and aggressiveness and impulsivity problems. Dr. Buitelaar takes an integrative strategy that includes clinical and phenotypic research, neuroimaging, cognitive research, genetics, pharmacology, and preclinical techniques. Rubia, K is a professor at King's College London, and his expertise is the pharmacological imaging of ADHD drugs and neurotransmitter manipulations, PET imaging in adults with ADHD, and so on. Franke B comes from Radboud University Nijmegen Medical Center. Dr. Franke B studies the influences of genetic factors on mental health problems and traits such as ADHD and focuses on understanding the biological mechanisms explaining the effects of such factors on the etiology of mental problems. In addition, we found that these three authors were members of the World Federation of ADHD and the ENIGMA-AHDH Working Group. Buitelaar J is the vice president of the World Federation of ADHD, while Franke B is a co-founder of ENIGMA. Through these organizations, researchers can strengthen communications and cooperation between different countries or institutions in this field. Scientific researchers including these three authors published the “The World Federation of ADHD International Consumption Statement” in 2021. Through an in-depth study of more than 2,000 participants, the authors came up with 208 specific descriptions of ADHD, which pointed out seven pieces of evidence-based evidence about the brain difference of ADHD  The rs-fMRI can be used to investigate spontaneous brain neural activity during the rest state without requiring the preformation of any task  Machine learning: at present, guardian interview and clinical observation are mainly used to determine whether a child suffers from ADHD, which may vary greatly because of regional divergence. If a single scale or indicator is used to diagnose or predict ADHD, the reliability will be insufficient. Therefore, we suggest that some data (such as sociodemographic information and neuroimaging) can be used to assist except the scale and relevant indicators. Increasing evidence shows that machine learning (ML) can be applied to neuroimaging data for the diagnosis and efficacy prediction of ADHD  Functional connectivity: ADHD is considered a neurodevelopmental disease with “brain disconnection,” including structural and functional connectivity disorder. Functional connectivity (FC) analyses are a common method of analyzing fMRI data  Network: In recent years, there has been increasing acceptance that ADHD is a distributed functional network disorder. Patients with ADHD suffer from different degrees of alternation in default mode network (DMN), frontoparietal network (FPN), ventral attention network (VAN), dorsal attention network (DAN), affective network (AN), somatosensory network (SSN), and visual network Rubia, . HoweverReferences with the strongest CB published recently may help us to investigate the research hotspots in this field. Listed in The latest valuable cited literature provides valuable information for future research directions. Hoogman et al.  used extThe current research is subjected to some limitations. First, only articles from the WoS and English articles were included, which might lead to bias in language and publication. Second, some studies unpublished or published newly were not included in this study. Third, the number of citations and centrality of the articles might vary depending on the particular time when the search was conducted. However, it is certainly believed that it has clarified the overall condition, emerging trends, and future directions of studies on ADHD, showing high reference values and providing some valuable suggestions for further study.In conclusion, this study revealed the status of relevant studies, hot topics, and trends concerning neuroimaging in ADHD from 1992 to 2022. During the past three decades, the number of relevant articles has increased significantly, which suggests that neuroimaging in ADHD has a positive development prospect. Most ADHD neuroimaging articles are from developed countries, and the authors are encouraged to develop close cooperation with others from various institutions and countries. Notably, “resting-state Fmri,” “machine learning,” “functional connectivity,” and “network” may be the hot topics in this field. With the rapid development and maturation of brain imaging technology, scholars could conduct more relevant research about the diagnosis and prognosis of ADHD.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.JW and NS contributed to conceiving, designing, and revising the manuscript. HD and ZH contributed to the analysis and manuscript writing. ZL, LC, and YH contributed to data collection. JW and YZ contributed to manuscript revision. All authors contributed to the article and approved the submitted version.


Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD. Attention deficit hyperactivity disorder (ADHD) is a neurobehavioral disorder marked by a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development. By definition, the symptoms are present in two or more life contexts, and there is clear evidence that they interfere with or reduce the quality of social, school, or work functioning . AlthougThe study of connectomics, which focuses on analyzing the brain’s network structure, is shedding light on the underlying causes of neuropsychiatric disorders such as ADHD. As more and more research is conducted in this field, novel discoveries are leading to greater understanding and advancements. Recognizing the fundamental value of dynamical changes in network functioning, the lens of connectomics is attractively orienting its focus from the study of the resting state to the analysis of time variation of brain functioning. Specifically, the study of brain dynamics is trying to translate some key concepts, such as those of adaptability and flexibility, commonly used by clinicians to define mental health, into quantifiable measures able to characterize normal brain functioning and differentiate physiological from pathological conditions ,8. ConceSleep EEG connectivity is much less studied in this field, even if the association between sleep disorders and ADHD is well-known in clinical practice and currently reported in the literature . Studies discussed in the literature suggest that the borders between cortical and sleep maturation are blurred in ADHD and that further investigations are needed to better understand the relationship between sleep and neuropsychiatric symptoms. In cognitive neuroscience, spindle activity, the physiological electroencephalographic hallmarks of non-REM (NREM) sleep microstructure, have been most studied as particularly involved in synaptic remodeling. Sleep spindles are defined as trains of distinct waves with frequencies of 11–16 Hz (most commonly 12–14 Hz) and a duration of >0.5 s . Known tAlterations in spindling profile have been linked to different neuropsychiatric disorders, suggesting that spindle hyper- and hypofunction can both contribute to the generation of clinical symptoms and, in some cases, could also play a decisive role in the etiology of the disease ,27,28,29Aiming to fill this gap, we conceived a preliminary study on sleep EEG sensor-space connectivity in children with ADHD, focusing on spindle activity. The study included eight  children with ADHD and nine  healthy children ranging in age from 7 to 11 years . All healthy and ADHD children were recruited among children admitted to the Neuropsychiatric Department of the Institute for Maternal and Child Health “Burlo Garofolo” . The diagnosis of ADHD was made according to DSM-5 criteria [The EEG was recorded by means of nineteen surface electrodes placed according to the 10–20 International System. The signals were acquired using a computerized system  with the earlobe reference. EEG signals were acquired at a sampling frequency of 512 Hz, and during acquisition, the impedances were kept below 5 kΩ. Subsequently, EEG signals were digitally filtered with the second-order Butterworth band-pass filter within the 0.5–45 Hz frequency range. EEG was recorded in the early afternoon after sleep deprivation. The day before, the children were asked to go to sleep later than usual (after midnight) and wake up early (5:00–6:00 a.m.). Acquisition started at about 01:30 p.m. and lasted about 60 min. All children fell asleep during the EEG recording. Only the 2nd stage NREM sleep phase was included in the analysis. In particular, EEG recordings were visually inspected by two of the trained authors (M.C. and C.Z.) to identify the 2nd stage NREM sleep phase and the start and end points of each spindle free of artifact. To confirm the identification of spindle parameters, the inspection of EEG was performed twice and separately by each of the mentioned authors. Sleep phases were identified according to AASM criteria .In order to make the analysis of spindle dynamics easier and more reliable, we developed by means of PSD a semi-automatic method able to identify the three epochs only requiring the visual identification of the spindle. In particular, for each spindle, epochs lasting 1 s were selected as follows: first, the maximum power peak in the sigma band (11–15 Hz) of the Fz channel was calculated and defined as the center of the during (E2) spindle epoch; the EEG was then cut 0.5 s before and after the maximum power peak in order to obtain the spindle epoch (E2). Next, the times corresponding to when the power reached 95% of the maximum power peak before and after the spindle were determined. Based on these times, one-second epochs were extracted to yield the epochs before (E1) and after (E3) the spindle.In order to control the non-overlapping of the epochs between two consecutive spindles, the epochs have been visually inspected. The number of spindles in each subject varied from 6 to 10. For each subject, every single spindle was considered as a trial of the same stochastic process. In order to account for sample size bias and to obtain a more robust and reliable estimate, the bootstrap method ,32 was ti and channel j is a frequency-domain approach that describes the directional flow of information from channel j to i at a frequency f, normalized by the total outflow information from j to the other channels [The coefficients of the models were used to calculate the Generalized Partial Direct Coherence (gPDC) values. PDC between channel channels . Previouchannels ; a signichannels . We usedThe gPDC has been performed on the full set of spindles separately for each epoch of a single subject, using an appropriate routine from SIFT Matlab toolbox ,37 in oriin) and out (Kiout) node degrees, the cluster values of single channels (Ci), the mean cluster (C) value and the shortest path length (L).Binary connectivity matrices were obtained by applying the thresholds to each gPDC matrix of epochs and bands of the original subjects ; meanwhile, the comparison between pairs of epochs was evaluated by applying the Wilcoxon sign rank test for dependent samples on connection number, C and L parameters. Differences with a p-value < 0.05 were defined as significant, and in the case of the analysis among epochs, the Bonferroni correction for multiple comparisons was applied.In order to evaluate statistical differences between ADHD and controls, the Wilcoxon rank sum test for independent samples was applied to all the parameters  compared to all other bands, mostly in the control group . The NC The NC in all three epochs was statistically different between the two groups in the delta and gamma band: it was higher in the delta band in the ADHD group and lower in the gamma band. In theta band, the difference was significant only in E2 . Since the main differences between groups were present in the delta and gamma bands, the graph analysis was focused on these two bands other than on the sigma band (the spindle band).iin and Kiout values obtained by averaging them among channels in the gamma band for each subject and epoch in order to evaluate the possibility of distinguishing the two groups by only using six variables. Finally, Fischer’s linear discriminant rule was used to find the line that divided the two groups based on the first two components.The Principal Component Analysis (PCA) method was applied to Kiin and “out-degree” Kiout showed different behaviors during spindle in the three bands  to high frequency (gamma) bands. In low frequencies, the values were lower in the controls than in ADHD, while in high frequencies, values in the controls were higher than in ADHD. In the sigma band, the values between the two groups were less segregated. The dynamic modifications across epochs showed a quite similar distribution of Kiin and Kiout within each group in delta and gamma bands.The analysis of nodes’ degree and, in particular, the relation between “in-degree” Kee bands . The reliin and Kiout tend to cluster in quite separate “clouds”. This segregation is more evident in the gamma band.However, in these bands, the distributions of Kiin and Kiout during the E2 epoch slightly increases, but the distribution of Kiin and Kiout shows less differentiation. Concerning differences in the Kiin and Kiout values of single channels, we did not identify specific topographic patterns in any band.In the sigma band, the number of Kiin and Kiout. These means were obtained by averaging the values of all channels and epochs for each subject and for each band. The findings revealed that only in the gamma band the first two PCA components were able to clearly distinguish the two groups  and Path Length (L) values showed different dynamics depending on the frequency band, even if with a similar inter-epochs trend in the two groups a. Significant differences for C and L values between the two groups in all three epochs were found in the delta and gamma bands, while in the sigma band, the differences, although present, were not significant. In particular, C values were higher in ADHD than in the controls in the three epochs of the delta and sigma bands, while in the gamma band, the highest values were observed in the controls. On the other hand, L values showed an opposite behavior in all the epochs and bands. Furthermore, C values showed significant differences among epochs only for ADHD between E1 and E2 in the delta band and between E2 and E3 in the gamma band. In the sigma band, both C and L values showed significant differences between E2 and the other two epochs for both ADHD and controls, with the highest C values and lowest L values in E2 in both groups. i  sleep EEG functional connectivity, analyzed as related to spindle activity, shows significant differences in children with ADHD; (2) differences in connectivity parameters depend on frequency bands; in particular, the gamma band mostly distinguish children with ADHD from children with typical neurodevelopment. These findings are discussed in detail in the following subchapters.Previous studies that analyzed connectivity in ADHD were carried out in a resting state or after a specific task ,43,44,45The results of our study show that connectivity parameters, such as the number of connections (NC), clustering coefficient (C) and path length (L), calculated in different epochs of N2 sleep, highlight dynamic modifications related to spindle activity and allow to identify the three phases  and to differentiate ADHD from controls.In particular, in the sigma, theta, alpha and beta bands, the number of connections increases during the spindle  and, in iin and Kiout degrees  in controls showed lower values in the delta band and higher values in the gamma band. Furthermore, in the gamma band, the relation between K degrees  clearly In in vivo studies, gamma oscillations are demonstrated to emerge from the coordinated interactions of excitatory and inhibitory neurons . Gamma bWe found in the sigma band similar spindle-related connectivity dynamics in the two groups, with an increase of NC during spindle and opposite trends of C and L values . It seems that, in both groups, when the neuronal system generates the spindle, the network better segregates (higher clustering coefficient) and integrates (shorter path length) information approaching a so-called small-world functioning , and thiIn the last years, in the field of connectomics, the concept of small-worldness has gained growing interest; as one of the most influential findings about brain functioning, it is considered that human brain networks exhibit prominent small-world organization. Such a network architecture would facilitate efficient information segregation and integration at low wiring and energy costs, presumably resulting from the natural selection under the pressure of a cost-efficiency balance, undergoing continuous changes during normal development and exhibiting significant alterations in neurological and psychiatric disorders .Recently, this concept has been used to describe network organization of sleep EEG activity: a trend has been reported on the functional connectivity during sleep to move forward to an organization more similar to that of a small-world network, with different small-world properties and different trends in the EEG frequency bands .In ADHD, sleep difficulties have been mostly viewed as an epiphenomenon of an underlying maturational disorder ,54,55,56Recently, the presence of small-world variations across different frequency bands has been described with fMRI in resting state ,60,61, fIn our study, we found that compared to the sigma band, connectivity in the delta band seems less modified by the spindle activity, maintaining quite constant values of C and L, with a functioning approaching that of a small world in the control group. The gamma band show opposite dynamic changes in the two groups: in the control group, the network seems to move toward a small-world functioning , while in ADHDs there seems to be a weaker small-worldness . Globally considered, our findings seem to reveal simultaneous different dynamical functioning of the neural network to the arrival of the spindle, depending on the frequency band. This could represent another aspect of the continuous modulation across different brain states of cortical connectivity during sleep, as hypothesized by Vecchio et al. . DynamicAccording to Steriade et al., slow oscillations during sleep have the ability to trigger and group faster cortical oscillations such as spindles and high-frequency activities in the beta or gamma range . It has Concerning C differences in the topography of single electrodes, we found that in ADHD, in the delta band, especially during the spindle epoch, C values of almost all the electrodes were higher than in the control group, while in the gamma band, C values were higher in the control group, in all the three epochs. No specific topographic pattern was found in any band. Spindles are hypothesized as originating from the thalamic reticular nucleus and rhythmic oscillations within the thalamocortical circuits, but little is known about their spatial properties and how they could influence functional connectivity of cortical and subcortical activities and modify brain networks organization and functioning. Recent works suggest that spindles are not homogenous, leaving unclear to what degree spindles are global or local and if their properties are uniform or location-dependent . It has Mills et al.  emphasizOur results, showing different spindle-related sensor-space connectivity dynamics in the two populations, in the delta but especially in the gamma band, raise questions about the involvement of the thalamus in ADHD and the role of gamma activity during sleep suggesting the presence in children with ADHD of an altered communication between the thalamus and the cortex, maybe mediated by an imbalance in the way delta and gamma activity interact.As a preliminary study, we are aware of its limitations and careful in overgeneralizing results. Mainly the low number of subjects and the non-consistent EEG connectivity methodological framework across the literature ,69 make However, to the best of our knowledge, there are no previous studies on sleep spindle connectivity in children, and the results of this study encourage us to further investigate this field, looking for a possible biomarker of pathological brain functioning in ADHD.In conclusion, connectivity methodologies focused on EEG microstructural elements have not yet been widely addressed and could possibly be able to provide valuable insight into this field.The present preliminary study’s results encourage further investigation of sleep EEG functional connectivity, particularly its dynamics related to spindle activity. If confirmed in larger clinical studies, these results could support the usefulness of an easily achievable, not invasive, and low expensive method to helping clinical diagnosis of ADHD. The study suggests the coexistence of different spindle-related functional connectivity features depending on frequency bands, raising questions about the involvement of the thalamus in ADHD and the role of gamma activity during sleep. Traditionally considered a hallmark of the normal organization of brain functioning and development, sleep spindles could be conceptualized and analyzed as a new window to study brain networks’ adaptability to internal as well as external events.


Cumulative exposure to psychosocial adversity at an early age has been shown to be a risk factor for attention-deficit hyperactivity disorder (ADHD) and autism that often co-occur. However, it is not clear if this association reflects a causal effect or familial confounding. We aimed to assess whether cumulative psychosocial adversity in the family increases the risk for ADHD and autism in offspring while accounting for unmeasured familial confounding. We used a population-based cohort of 1,877,901 individuals born in Sweden between 1990 and 2009. Participants were followed from the age of 3 until 2013, with a median follow up time of 13.8 years. We created a cumulative index based on 7 psychosocial adversity factors. We used Cox regression to estimate the hazard ratios (HRs) relating neurodevelopmental conditions to cumulative psychosocial adversity. To address familial confounding, the analyses were repeated in groups of relatives of different kinship: siblings and half-siblings and cousins. A dose-response relationship was observed between cumulative exposure to psychosocial adversity and ADHD at a general population level (covariate adjusted HRs (aHRs) with 95% confidence intervals ranged from 1.55  to 2.65 ). No clear dose-response relation was seen for autism (aHRs ranged from 1.04 [.59–1.84] to 1.37 [1.30–1.45]). HRs of ADHD and autism decreased with increasing level of kinship in the analysis of relatives. Cumulative exposure to psychosocial adversity was associated with both ADHD and autism in the general population, these associations were partly explained by unmeasured familial confounding between relatives. This highlights the need for using family-based designs in studies of psychosocial adversity and ADHD and autism. Attention-deficit hyperactivity disorder (ADHD) and autism are common neurodevelopmental conditions, which first manifest in early childhood and often co-occur . Both ADA scale for psychosocial adversity created by Rutter et al. consists of several factors: severe marital discord, low socioeconomic status, large family size, paternal criminality, maternal mental condition, and foster care placement . Based oWhen attempting to establish causal associations, residual confounding through both genetic and environmental factors must be considered. One major limitation of previous studies of psychosocial adversity and neurodevelopmental conditions is that most were unable to adjust for unmeasured familial  confounding. This is critical because psychosocial adversity , as wellBy choosing the first year of life as an exposure period, we are minimizing the risk of reverse causation. Families of children with neurodevelopmental conditions experience higher levels of stress and are more likely to experience psychosocial adversity . This isMoreover, ADHD and autism often co-occur; the rate of ADHD in individuals with autism varies from 25.7% to 70% , and autThe aim of this study was to investigate the association between cumulative psychosocial adversity in the family during the first year of life and neurodevelopmental conditions in offspring, using family-based study designs to control for unmeasured confounding, and to assess if psychosocial adversity is specifically associated with ADHD or autism.Data for this study were obtained by linking different Swedish population-based registers using personal identity numbers : the Totn = 2,042,257) as well as their parents and followed them until 31 December 2013. We excluded individuals who were stillborn or had major congenital malformations, twins, everyone who died or migrated during the first year of life, and individuals with missing information on their own or their parents’ personal identity number. After exclusion, the analytical sample consisted of 1,877,901 individuals. Among these, we identified subgroups of full siblings  for the sibling comparison, half-siblings  and full cousins .Using these registers, we identified all individuals born in Sweden between 1 January 1990 (due to information availability from LISA) and 31 December 2009  during the child’s first year of life, based on the Total Population Register.Parental divorce during the child’s first year of life, defined as “yes” if the family situation has changed based on information from LISA the same year.Parental financial problems during the child’s first year of life, defined as “yes” if the family had low economic status, based on information from LISA the same year. Low economic standard was defined as a household disposable income of less than 60% of the median income in the whole population in that particular year  SAS Institute Inc). Analysis was performed in Stata . This study was approved by Stockholm’s Regional Ethical Review Board (Dnr 2013/862–31/5). Code availability: see supplementary materials.Descriptive characteristics of the study population are presented in Table In the general population, the cumulative psychosocial adversity index was associated with ADHD in a dose-response relationship, with adjusted HRs for ADHD ranging from 1.55 to 2.65  . The interaction analysis showed that cumulative psychosocial adversity increases the risk of ADHD regardless of autism. However, the effect was stronger in individuals with ADHD only , compared to those with co-occurring ADHD and autism  (Supplementary Table The association between cumulative psychosocial adversity and ADHD was modified by autism (The sensitivity analysis that aimed to account for the diagnostic uncertainty included only those who received at least 2 diagnoses of ADHD/autism showed no difference for ADHD and autism compared to the main analysis (Supplementary Table Separate analysis with stratification by sex showed that boys and girls had similar risks of developing ADHD with the dose-dependent effect related to experiencing cumulative psychosocial adversity (Supplementary Table The main objective of this population-based study was to identify whether cumulative exposure to psychosocial adversity early in life increases the risk of ADHD and autism in children. Our findings on the general population level are in line with several previous studies showing that exposure to psychosocial adversities and traumatic events elevate the risk of ADHD and autism , 32, 33.Importantly, for both outcomes, the estimates were attenuated towards the null with increasing level of familial relatedness, indicating that the associations were likely influenced by unmeasured familial confounding. Moreover, cumulative exposure to psychosocial adversity increased the risk of ADHD regardless of comorbidity with autism, but with a stronger effect among those without autism. For autism, the risk increase was observed only among individuals without ADHD.In contrast with our findings at the population level, the most recent meta-analysis  emphasizThis study has several strengths. It is population-based, and includes all children born in Sweden over a 20-year period. We used register-based data of high quality, with coverage of 85%–100% depending on the register , 28. TheThere are also several limitations to consider. Information on some of the psychosocial adversity factors included in the study, such as parental income and education, is based on register data that is updated once a year, which makes the timing of exposure somewhat imprecise. This might have influenced some sibling pairs where children were born close in time, resulting in siblings being concordant on exposure and therefore not contributing substantially to the analysis. The information about family situation only reflects those who separated after being married. We therefore had no information on separation of cohabiting partners, which is a common form of relationship in Sweden. Register-based data does not allow us to distinguish between symptomology and quantitative traits. Neither does it allow to explore the level of perception of experienced psychosocial adversity; therefore, we were not able to disentangle whether some factor had more impact, and these factors were equally weighted. Further studies with more detailed information on both exposure and outcome, including e.g., severity of symptoms, are clearly needed. The analyses on autism were hampered by the low number of children exposed to 4 or more factors, who also had autism. Therefore, we interpreted these results with caution, and in view of the overall pattern of results. Information on diagnoses in outpatient care was only available from 2001 and onwards. Since we only considered diagnoses after age 3, only individuals born 1999 or later have complete coverage in the outpatient register, which might lead to a misclassification of the outcome among those who were born before 1999. However, when we restricted the analyses to those who were diagnosed after 2001 the results were similar to the main analysis. There is also a possibility of carryover effects, where a younger sibling might be more likely to get diagnosed earlier if their older sibling has received a diagnosis of ADHD or autism . HoweverThis study may encourage clinicians and especially general practitioners to pay more attention to families that experience psychosocial adversity. If the association between psychosocial adversity and neurodevelopmental conditions is explained by familial confounding, the focus should be kept on early detection and intervention. This information can be used by policymakers and clinicians on vulnerable groups of children in need of more careful pediatric surveillance and screening for ADHD and autism. Early detection enables timely interventions, with the aim to prevent more severe psychiatric problems for children with ADHD and autism. The importance of familial confounding in this study highlights the need of further research into etiology of ADHD and autism to differentiate between the types of familial confounding, including genetic and shared environment.This is the first population-based study investigating the association between cumulative psychosocial adversity and both ADHD and autism, while accounting for unmeasured familial confounding. Our findings show that being exposed to cumulative psychosocial adversity in the family during the first year of life is associated with a greater risk of developing ADHD and autism. Associations were weaker among related individuals, indicating that they were at least partly explained by unmeasured familial confounding. The findings highlight the importance of accounting for unmeasured genetic and shared environmental confounding when studying the effect of psychosocial adversity on neurodevelopmental conditions , 40. We Supplementary materials


Is adult attention-deficit/hyperactivity disorder (ADHD) associated with an increased risk of dementia?In this national birth cohort study, 109 218 participants with or without a diagnosis of adult ADHD were followed up for 17.2 years for dementia. The presence of adult ADHD was statistically significantly associated with an increased risk of dementia.This study suggests that adult ADHD is associated with an increased risk of dementia and warrants reliable assessment in adulthood. Evidence that adult attention-deficit/hyperactivity disorder (ADHD) is associated with an increased risk of dementia is scarce and inconsistent, and potential sources of bias are untested.To examine the association between adult ADHD and the risk of dementia.This prospective national cohort study consisted of 109 218 members of a nonprofit Israeli health maintenance organization born between 1933 and 1952 who entered the cohort on January 1, 2003, without an ADHD or dementia diagnosis and were followed up to February 28, 2020. Participants were aged 51 to 70 years in 2003. Statistical analysis was conducted from December 2022 to August 2023.International Classification of Diseases, Ninth Revision, and the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision); otherwise, absent.Adult ADHD was a time-varying covariate, classified as present from the age of the first diagnosis (using the Cox regression models were fitted to quantify the association between adult ADHD and the risk of incident dementia with hazard ratios (HRs) and their 95% CIs unadjusted and in the primary analysis, using inverse probability weights, adjusted for 18 sources of potential confounding. In 14 complementary analyses, subgroup and sensitivity analyses were implemented.P < .001) associated with an increased dementia risk . Twelve of the 14 complementary analyses did not attenuate the conclusions based on the results of the primary analysis. There was, however, no clear increase in the risk of dementia associated with adult ADHD among those who received psychostimulant medication, and evidence of reverse causation was mild.At the beginning of the follow-up, the sample of 109 218 participants had a mean (SD) age of 57.7 (5.5) years, 56 474 participants (51.7%) were female, and 52 744 (48.3%) were male. During follow-up, 730 participants (0.7%) received a diagnosis of adult ADHD, and 7726 (7.1%) received a diagnosis of dementia. Dementia occurred among 96 of 730 participants (13.2%) with adult ADHD and 7630 of 108 488 participants (7.0%) without adult ADHD. In the primary analysis, compared with the absence of adult ADHD, the presence of adult ADHD was statistically significantly (In this cohort study of individuals born between 1933 and 1952 and followed up in old age, adult ADHD was associated with an increased risk of dementia. Policy makers, caregivers, patients, and clinicians may wish to monitor reliably for ADHD in old age. This cohort study examines the association between adult attention-deficit/hyperactivity disorder and the risk of dementia. However, the association is null in some studies18 and is stronger among males than females.17Adult ADHD may be associated with an increased risk of dementia based on common health outcomes, clinical observations, family-based research, and epidemiologic risk studies. Meta-analyses have identified 6 health outcomes 19 Reverse causation challenges the association between adult ADHD and dementia because adult ADHD is accompanied by cognitive impairments that resemble dementia and coincide with the onset of the protracted preclinical phase of dementia , a nonprofit health maintenance organization (HMO) that provides health care services with national coverage to 14% of the total population of Israel.20 In brief, it is illegal for nonprofit HMOs in Israel to refuse membership based on demographic factors, health conditions, or medication needs, thereby limiting sample selection bias in the present study. These data at Meuhedet include every patient with a clinical diagnosis of dementia since 2000. The institutional review board at the University of Haifa and the Meuhedet-associated Helsinki institutional review board granted ethical approval to conduct the present study with a waiver of written informed consent because the data were deidentified. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.The present cohort study source population was ascertained from electronic health records held at Meuhedet Healthcare Services 21 (codes 331.0-331.9) and the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)22 (codes F00-F03). The estimated prevalence of dementia in this HMO is 6.6%,23 which resembles estimates in Western Europe (6.9%) and the US (6.5%).24 The diagnosis of dementia is ascertained by geriatricians, neurologists, or psychiatrists and has been used in previous descriptive23 and analytic25 epidemiologic research.The Meuhdet diagnoses of dementia are based on the ICD-9 (code 314) or ICD-10 (code F90) codes from January 1, 2003, to February 28, 2020. Each ADHD diagnosis is assigned by a psychiatrist, neurologist, or clinical neuropsychologist, all of whom are board certified. An ADHD diagnosis may also be assigned by a board-certified pediatrician, general practitioner, or family physician, who must undergo a Ministry of Health–certified course on ADHD diagnosis. An ADHD diagnosis requires a standardized neuropsychological assessment, typically done in testing centers specializing in ADHD.Diagnoses of ADHD were ascertained based on 26 These covariates were background information, comorbid health conditions, and use of stimulant medication. The background information was the age at the beginning of the study in 2003 (entered as linear and quadratic terms),27 sex , and neighborhood socioeconomic status . Smoking status was a time-varying covariate that was classified as present from the report of smoking forward and otherwise classified as absent. Comorbid health conditions were all ascertained as time-dependent covariates and classified as present from the first diagnosis onward, otherwise classified as absent. These comorbid health conditions were depression, obesity, chronic obstructive pulmonary disease, hypertension, atrial fibrillation, heart failure, ischemic heart disease, cerebrovascular disease, diabetes, Parkinson disease, traumatic brain injury, migraine, and mild cognitive impairment  Classification System codes classified as psychostimulants, agents used for ADHD, and nootropics .29Statistical analysis was conducted from December 2022 to August 2023. Participant characteristics were computed to characterize the source population and present differences between the group with and the group without adult ADHD for the study covariates. Next, rates per 10 000 person-years of follow-up were estimated.30 All models were implemented with age as the underlying timescale.31 Inverse probability weights were used in the adjusted models to address residual confounding.32 This approach incorporated time-independent and time-dependent covariates. To generate the inverse probability weights, the covariates of the primary model were included with ADHD as the outcome. The adjusted models included all the study covariates and inverse probability weights to improve the control of confounding. Also, the age-sex distribution was calculated in the study sample as the proportion of the Israeli national population, and the analysis was weighted to represent the entire population of Israel.33 Each participant was followed up for a dementia diagnosis, leaving the HMO, all-cause mortality, or the end of follow-up, whichever came first  for dementia risk, with ADHD as a time-varying covariate. All HRs were presented with the associated 2-sided 95% CIs, using robust SEs.34 and plotted the cumulative incidence curves to present the risk of incident dementia for the groups with or without adult ADHD. The proportional hazards assumption was tested with the standard statistical test for the Cox proportional hazards regression model.34 The covariates of age at cohort entry (linear and quadratic), sex, and socioeconomic status were not time-varying covariates, whereas smoking, comorbid health conditions, ADHD medication, and the diagnosis of ADHD were time-dependent covariates. Models were fitted in R, version 4.1 , using the survival34 and ipw libraries.32 All P values were from 2-sided tests, and results were deemed statistically significant at P < .05.The groups with or without adult ADHD were contrasted unadjusted (with only the binary covariate of ADHD), and then in the primary model adjusted for the study covariates. For the unadjusted model, we estimated the cumulative incidence proportion17 and smoking status (model 2) and was restricted to different age groups at the beginning of the study: aged 60 years or younger (model 3) and older than 60 years (model 4). In a sensitivity analysis, we examined the risk of dementia up to and then older than 65 years of age separately to distinguish between early-onset dementia (model 5) and late-onset dementia (model 6).3The robustness of the results from the primary analysis was challenged in 14 complementary analyses. To consider subgroups, owing to possibly differential dementia risks, the primary analytic model was refitted to examine the ADHD interactions with sex (model 1)Next, we examined aspects of ADHD in sensitivity analyses. To consider diagnostic reliability, we compared the group without ADHD with individuals with 1 adult ADHD diagnosis (model 7) and more than 1 adult ADHD diagnosis (model 8). We examined ADHD diagnosis as a static covariate (model 9), as ADHD is generally considered a neurodevelopmental disorder originating in early life. The adult ADHD interaction with psychostimulant medication exposure was examined (model 10). Among individuals with adult ADHD, groups exposed vs unexposed to psychostimulants were compared (model 11).35 the analysis was stratified by sequential durations of follow-up. Based on 3 successive 5-year intervals, the associations between ADHD and dementia risk were scrutinized (models 12-14). Attention-deficit/hyperactivity disorder was assumed to have a weaker association with dementia risk in the preclinical dementia stage when the assessment was performed long before the onset of dementia and to have a stronger association with dementia risk when the assessment was performed nearer to the dementia diagnosis. A stronger association in the later intervals rather than the earlier intervals of follow-up suggests that reverse causation occurs.Finally, in sensitivity analysis, to examine reverse causation,At the beginning of the follow-up, the sample of 109 218 participants had a mean (SD) age of 57.7 (5.5) years, 56 474 participants (51.7%) were female, and 52 744 participants (48.3%) were male . The groThe follow-up period for incident dementia was 17.2 years. During follow-up, 730 participants (0.7%) received a diagnosis of adult ADHD, and 7726 (7.1%) received a diagnosis of dementia. A total of 163 of the 730 participants with ADHD (22.3%) received psychostimulant treatment . The per2 = 2.42; P = .12). In the primary analysis, the group with adult ADHD had a statistically significantly (P < .001) higher risk of incident dementia  and smoking status (model 2) and restricted by age at the beginning of the study to 60 years of age or younger (model 3) and older than 60 years of age (model 4). These results did not attenuate the conclusions of the primary results . In sensTo examine reverse causation, the analysis was stratified by 3 sequential 5-year successive follow-up durations. The models did not attenuate the conclusion based on the results of the primary model. However, the closer the duration was to the dementia diagnosis, the higher the HR that quantified the association between ADHD and the risk of dementia . The ADHD medication and diagnosis combination is 0.5% (567 of 109 218), possibly an underestimate of adult ADHD. Also, psychostimulants are cognitive enhancers hypothesized to reduce dementia risk.19 In addition, our study results should be interpreted within the broader literature that generally supports the association between adult ADHD and dementia.17 Seen this way, including psychostimulants in the association between adult ADHD and dementia risk may account for confounding by medication. That said, future research is necessary, including the assessment of adult ADHD symptom severity, to examine the role of psychostimulant medication in old age.There are different interpretations of the finding that adult ADHD treated with psychostimulant medication was not clearly associated with an increased dementia risk. It is possible that ADHD treated with medication reflects more severe ADHD compared with ADHD not treated with prescribed psychostimulants, which may reflect milder symptoms or even a less-accurate diagnosis. Also, ADHD is a chronic condition and may have negative long-term sequelae when untreated. Hence, the lack of association among individuals exposed to psychostimulants contradicts the conclusion that ADHD is associated with dementia risk. Alternatively, one may accept our ADHD ascertainment procedures. Among patients with adult ADHD, psychostimulant exposure is uncommon . In our study, we documented aspects that were potentially associated with the reliability of the ascertainment of ADHD. Namely, we pointed out the requirements of professional training, neuropsychological testing, and clinical impressions. In sensitivity analysis, we observed that the results were not attenuated by considering individuals with single or multiple ADHD diagnoses. Nonetheless, this study relies on clinical, not research, diagnoses.Our study has noteworthy limitations. We lacked information on childhood-onset ADHD. However, adult and childhood ADHD groups have different profiles,37 and benefits 38 to weigh before considering psychostimulant medication as a dementia prevention strategy. Future research is warranted to study the possible efficacy and adverse effects of psychostimulant medication in old age, before translation to the clinic can be considered.12There was no clear association between adult ADHD and dementia among individuals with ADHD who received psychostimulant medications. Due to the underdiagnosis of dementia as well as bidirectional misdiagnosis, this association requires further study before causal inference is plausible. There are multiple risks 24 Attention-deficit/hyperactivity disorder may be associated with dementia owing to other factors . However, the study covariates were chosen based on conditions that were previously found to be associated with ADHD and dementia .Dementia was most probably underascertained in our study. It seems, however, unlikely that the underascertainment of dementia would reduce the effect sizes of the study risk estimates to null. Also, the rate of dementia in our data resembles that in other nations.In our data, it was not possible to assess lifelong low academic achievement and cognitive reserve. Also, we could not distinguish cognitive impairment due to lifelong low academic achievement that may result in an impaired cognitive reserve or due to incipient later-life neurodegenerative or cerebrovascular disease. Based on observational data, such as those in our study, causal inference is traditionally considered not possible. Nonetheless, we implemented inverse probability weighting to account for confounding bias. In addition, the increase in the association between ADHD and dementia at shorter lag times in the analysis of reverse causation suggests that there may be some diagnostic overlap. However, the reverse causation analyses did not attenuate the results of the primary analysis at any time interval.In this cohort study of 109 218 participants followed up to 17.2 years, after adjustment for 18 potential sources of confounding, the primary analysis indicated that an adult ADHD diagnosis was associated with a 2.77-fold increased dementia risk. Complementary analyses generally did not attenuate the conclusion of the primary analysis. This finding suggests that policy makers, caregivers, patients, and clinicians may wish to monitor ADHD in old age reliably.


Evidence about the etiology of the predictive associations between adiagnosis of ADHD and cognitive performance over time is scarce. Here, weexamine these predictive and etiological patterns using a cross-lagged modeldesign in a sample of 404 participants  from ADHD and controlsibling pairs aged 6 to 17 years at baseline and 12 to 24 years atfollow-up.Data included IQ, short-term and working memory measures, and response speedand variability from a four-choice reaction-time task.ADHD and IQ predicted each other over time. ADHD at baseline predicted lowerworking memory performance at follow-up. Stable etiological influencesemerged in the association between ADHD and cognitive variables acrosstime.Whether early interventions can reduce negative interference with learning atschool requires further study. Attention deficit hyperactivity disorder (ADHD) is a highly heritable disorder,characterised by developmentally inappropriate levels of inattention andhyperactivity-impulsivity . ADHD afLongitudinal studies using repeated measurements of cognitive impairments fromchildhood to adolescence or young adulthood have broadly shown that individuals withpersistent ADHD display persisting impairments in RTV, IQ, and measures ofinhibition and working memory .FindingPossible explanations for these mixed findings are differences in study design andmeasures, including whether measurement of ADHD symptoms relied on parent orself-reports and whether functional impairment was part of the measurement. Also,these studies focused exclusively on investigating the prediction of later ADHD byexamining early cognitive functioning, but there are different ways in which ADHDand cognitive functioning can influence each other over time. For instance, earlyADHD may predict later cognitive impairments over time. In this case, we canhypothesise that the behavioral symptoms of ADHD in childhood may contribute to theworsening of later cognitive impairments. Evidence from longitudinal studies showsthat high levels of ADHD symptoms in early childhood predict poor reading andmathematics skills, and lower executive functioning skills in childhood,adolescence, and young adulthood . AlternaA cross-lagged model allows the simultaneous examination of longitudinal influencesof one variable on another, and vice versa, while also controlling for concurrentassociations between variables over time. Despite several studies havinginvestigated the relationship between ADHD and cognitive performance over time, toour knowledge, only a few studies to date have applied the cross-lagged design totest this association . These sTwin and sibling studies enable us to move beyond observable phenotypic associations,to explore their etiology. Such quantitative genetic studies indicate substantialgenetic and familial risk influences underlying the cross-sectional associationbetween cognitive functioning and ADHD symptoms or diagnosis. Population-based twinstudies in children show substantial overlap of genetic influences between ADHDsymptoms and response speed (mean reaction time [MRT]), RTV, inhibition,working-memory performance (digit span backward [DSB]) and IQ .SimilarThe first aim of this study is to investigate longitudinally the direction of theassociation between ADHD diagnosis and cognitive performance that previously showedstrong cross-sectional associations with ADHD using the same sample used in thisstudy .Using Participants aged between 6 and 17 years were recruited from specialist clinicsin the UK from among those who had a clinical diagnosis of DSM-IV combinedsubtype ADHD during childhood. Closest-age siblings were also then recruited andassessed for ADHD. A control group, which was initially recruited from primary(ages 6–11 years) and secondary (ages 12–18 years) schools in the UK was alsoSupplementary material. Participants whose ADHD diagnosischanged from baseline to follow-up were retained in the analyses to account forthe stability and change in ADHD diagnosis over time.The sample retained at follow-up consisted of 404 participants. After exclusions of participants with missingdata, the sample included 391 participants: 99 participants with persistent ADHDand 100 unaffected siblings , 23 remitters, and 169 control siblings . Further information on the sample is given in Supplemental Table S1). A 48-hour ADHD medication-free periodwas required prior to assessments. All participants and parents providedinformed consent. Study procedures were approved by the London-Surrey BordersResearch Ethics Committee (09/H0806/58).There were no significant differences between the groups in age, but theydiffered significantly in sex, with more males in the ADHD group compared tounaffected siblings and controls and no females among remitters  of reaction times from the baseline and fast-incentivecondition).The task was a computerised four-choice reaction time (RT) task which measuresperformances under a slow-unrewarded and a fast-incentive condition . The sloA cross-lagged model enriched by analyses to fit sibling data was accomplished bystructural equation model (SEM) using the OpenMx package in R .The modAs siblings share on average 50% of their segregating genes and 100% of thecommon environment, we can decompose the variance/covariance of traits intocontributions of familial influences  and non-familial influences ; the cross-sibling/within-traitcorrelation (correlation between siblings in each pair) was fixed to 0.40 , while taking into account the pre-existingrelationship between variables at baseline. Estimates are defined as small(<0.30), medium (0.30–0.50), and large >0.50; . Separatc and d). Theregression coefficients of the cross-lagged paths are used to examine thedirection of the association between ADHD and each cognitive variable  across time. The stability pathsconnect the same measure across time points (a andb) and represent the contribution of a variable at time 1to the same variable at time 2 .The phenotypic cross-lagged paths  connect f, nf). Familial and non-familial influences onADHD diagnosis were fixed at time 1 and 2. At time 2, familial and non-familialinfluences on each variable are estimated as residuals, indicating the familialand non-familial contributions independent of the familial and non-familialinfluences transmitted from time 1 .At each time point, the contribution of familial and non-familial influences onDSB, DSF, MRT in both task conditions, and RTV in both task conditions arecalculated, giving an estimate of the time-specific familial and non-familialinfluences . Familial and non-familialcorrelations indicate the extent to which the familial and non-familialinfluences impacting on ADHD are the same as those impacting on one of thecognitive variables. At time 2, familial and non-familial influences on thecorrelations between variables are estimated as residuals, indicating theassociation between the variables independent of their relationship at time 1(time-specific for time 2).Time-specific familial and non-familial correlations between ADHD, and DSB, DSF,MRT, and RTV in both task conditions are estimated at each time point (f2 andrnf2) and covariance transmitted from time 1 . The transmitted covariance between ADHDand cognitive variable from time 1  iscalculated by summing all possible paths for each variable from time 1 to time2, divided by the total covariance of ADHD and cognitive variable at time 2.Covariance can be transmitted via the stability paths (paths aor b), via the cross-lagged paths (paths c ord), and via correlation paths . Asstandard procedure, we used unstandardised path coefficients to calculate thesepathways or routes .To examine the stability of familial and non-familial influences on theassociation between ADHD and cognitive variables over time, the covariancebetween ADHD and cognitive functioning was divided into covariance specific totime 2 . Stability paths for all cognitive variables weresignificant and moderate to large . The stability path for ADHD was significant ineach model and ranged from 0.66 to 0.73 .ADHD and IQ showed significant reciprocal associations over time as shown by the95% confidence intervals of the cross-lagged paths . The croSupplemental Table S4.Familial and non-familial influences on each variable at both time points arereported in At time 1, the familial and non-familial correlations between ADHD diagnosis andIQ, DSB, DSF, MRT in both task conditions, and RTV in both task conditions arereported in All residual familial and non-familial correlations were non-significant at time2 . ThissuFamilial and non-familial associations between ADHD and cognitive variables overtime are reported in At time 2 a high proportion of stable effects emerged in the familial covariationbetween ADHD and IQ (62% stable effects). The familial covariation between ADHDand DSB, between ADHD and DSF and between ADHD and RTV in the fast incentivecondition showed a high degree of time-specific effects (ranging between 69% and73% stable effects). An equal proportion of stable versus time-specific effectsemerged between ADHD and MRT in the baseline condition (48% time-specificeffects vs. 52% stable effects), between ADHD and MRT in the fast-incentivecondition (51% time-specific effects vs. 49% stable effects), and between ADHDand RTV in the baseline condition (57% time-specific effects vs. 43% stableeffects).Non-familial influences between ADHD and MRT in both conditions, and between ADHDand RTV in both conditions, showed a high proportion of stable effects at time 2(ranging between 67% and 77% stable effects). Time-specific non-familialinfluences emerged in the covariation between ADHD and DSB, and ADHD and DSF (75% and 77% time-specific effects respectively). An equal proportion stableversus time-specific effects emerged in the non-familial covariation betweenADHD and IQ (49% time-specific effects vs. 51% stable effects).Although the covariation between ADHD and cognitive variables at time 2 showedboth stable and time-specific effects, the residual estimated correlationsbetween ADHD and each cognitive variable were non-significant, suggesting thatonly stable effects significantly influence the association between ADHD and thecognitive measures at time 2.Using longitudinal assessments of ADHD diagnosis and cognitive performance inaffected and control sibling pairs, we found evidence of reciprocal associationsbetween ADHD diagnosis and IQ over time. Further, ADHD diagnosis in childhood andadolescence predicted impaired working memory (DSB), but not short-term memory(DSF), response variability (RTV), or response speed (MRT), at follow-up 6 yearslater. The shared familial and non-familial effects influencing the associationsbetween ADHD and cognitive measures in childhood showed stability over time,although significant time-specific familial and non-familial influences emerged forADHD and each cognitive impairment at follow-up.Our finding that ADHD diagnosis in childhood predicts impaired working memory inadolescence and young adulthood, over and above their relationship in childhood,suggests that childhood ADHD has a negative impact on future working memoryperformance. One possible interpretation is that children with ADHD, compared tochildren without the disorder, have more difficulties paying attention whileperforming daily and academic tasks, which may make encoding task information andretrieving it from working memory more difficult . As theWe further show evidence of reciprocal association between ADHD and IQ over time:ADHD in childhood predicted lower IQ scores at follow-up, and vice versa. Thisresult extends our previous uni-directional analysis that showed lower IQ atbaseline predicting later ADHD symptoms and impairment using a subset of the presentsample . Thisresult was supported by the significance of the correlations between ADHD and eachcognitive variable at baseline and at follow-up in the cross-lagged model, sinceonly significant correlations at baseline can show stability over time, whilesignificance of the correlations at follow-up would suggest significance of newtime-specific effects at follow-up. Given that the associations between ADHD andeach investigated cognitive variable at follow-up was non-significant, our resultsuggests that the new etiological influences emerging after 6 years do notcontribute significantly to the association between ADHD and cognitive performanceand that these associations are accountable by stable effects only. However, giventhe wide confidence intervals, these familial and non-familial correlations atfollow-up should be interpreted cautiously, and future studies using larger samplesare required to further examine these associations between ADHD and cognitivefunctioning over time.The following limitations should be considered when interpreting these findings.Given that this study focuses on sibling data only, it allowed the investigation offamilial and non-familial effects, but we could not directly estimate thecontribution of genetic factors. However, as previous evidence suggests a limitedrole for shared environmental influences on either ADHD  or cogniIn conclusion, our findings indicate reciprocal association between ADHD and IQ overtime and that childhood ADHD predicts future working memory deficits in adolescenceand young adulthood, but not future deficits in attention fluctuation, responsespeed, and short-term memory which instead co-occur with ADHD without influencingits outcome over time. We further provide evidence of stability of familial andnon-familial effects influencing the association between ADHD and cognitive measuresover time, which requires replications in bigger samples. Based on these findings,future studies may investigate how early interventions can best reduce the negativeinterference with learning at school and increase the opportunities for individualswith ADHD to cognitively benefit more fully from education.Click here for additional data file.Supplemental material, sj-docx-1-jad-10.1177_10870547231159908 for TheEtiological and Predictive Association Between ADHD and Cognitive PerformanceFrom Childhood to Young Adulthood by Isabella Vainieri, Giorgia Michelini,Celeste H. M. Cheung, Olakunle A. Oginni, Philip Asherson, Frühling Rijsdijk andJonna Kuntsi in Journal of Attention Disorders


Emotion dysregulation (ED) is a core intrinsic feature of adult presenting Attention Deficit Hyperactivity Disorder (ADHD). However, the clinical expressions of ED are diverse and several questionnaires have been used to measure ED in adults with ADHD. Thus, to date, the characteristics of ED in adult ADHD remain poorly defined. The objective of this study is to identify the different patterns of ED in adults with ADHD.A large sample of 460 newly diagnosed adults with ADHD were recruited. Patients completed a total of 20 self-reported questionnaires. Measures consisted in the several facets of ED, but also other clinical features of adult ADHD such as racing thoughts. A factor analysis with the principal component extraction method was performed to define the symptomatic clusters. A mono-dimensional clustering was then conducted to assess whether participants presented or not with each symptomatic cluster.emotional instability”, “impulsivity”, “overactivation”, “inattention/disorganization” and “sleep problems”. ED was part of two out of five clusters and concerned 67.52% of our sample. Among those patients, the combined ADHD presentation was the most prevalent. Emotional instability and impulsivity were significantly predicted by childhood maltreatment. The ED and the “sleep problems” factors contributed significantly to the patients’ functional impairment.The factor analysis yielded a 5 factor-solution, including “ED in ADHD is characterized along emotional instability and emotional impulsivity, and significantly contributes to functional impairment. However, beyond impairing symptoms, adult ADHD may also be characterized by functional strengths such as creativity. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder, concerning 4% of the adult population –3. ADHD Since the first descriptions of adults with ADHD, emotional difficulties have been considered as a main symptom of the disorder in adults. For instance, Wender et al.  considerRegarding the etiology of ED in ADHD, it has been demonstrated that ED is not due to a comorbid disorder, but it is rather intrinsic to ADHD , 40–43. gold-standard” presentation of ED. BPD is a highly impairing mental disorder characterized by a persistent pattern of unstable relationships associated with pronounced impulsive and self-harming behaviors, unstable identity and difficulties regulating emotions ).The brief Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire  is the The Racing and Crowded Thoughts Questionnaire-13-item  is a 13The Cognitive Processes Associated with Creativity  is a 28The Insomnia Severity Index  is a 7-The Epworth Sleepiness Scale  is a 8-The Childhood Trauma Questionnaire Short-form  was useThe Weiss Functional Impairment Rating Scale WFIRS; , 87), is, is87]),Oblimin) was applied. Factors were retained based on their eigenvalue (greater than 1) and after inspection of the scree plot. Only non-overlapping items with loadings greater than 0.40 were considered as being part of a factor. A mono-dimensional clustering was conducted for each principal component obtained to assess whether participants presented or not with each symptomatic factor [Descriptive statistics included frequencies and percentages of categorical variables together with means and standard deviations of continuous variables. To investigate the different possible symptoms clusters in our sample of adults with ADHD, a factor analysis with the principal component extraction method was performed . Considec factor . This mon = 106) experienced a past episode of depression and 15.1% (n = 41) have a comorbid bipolar disorder. 14.8% (n = 16) adults of our sample had an anxiety disorder, 2.05% (n = 8) presented with an obsessive–compulsive disorder, 8% (n = 40) presented a borderline personality disorder and 4.5% (n = 14) an autism spectrum disorder. Concerning medications, 67% (n = 154) of patients did not take any psychotropic drugs at the time of the recruitment. Among those under medication at recruitment, 1.28% (n = 5) had just started their psychostimulant treatment, 18.4% (n = 52) were treated with antidepressants and 10.3% (n = 29) with anxiolytics. Moreover, 9.90% (n = 28) of the sample were taking mood stabilizers, 7.1% (n = 20) antipsychotics and 2.8% (n = 8) were under hypnotics. Detailed demographic data are presented in Table Among the 391 retained adults with ADHD, the combined presentation was the most prevalent, concerning 73.40% of the group, followed by the inattentive presentation and the hyperactive one (22.9% and 3.7% respectively). Regarding psychiatric comorbidities, 34.1% , the five UPPS-P subscales, the BSL-23, the BDI, the five TEMPS-A subscales, the RCTQ-13, the CPAC, the ISI and the ESS. The analysis yielded a five factors solution explaining 60.1% of the total variance. Each factor presented with an eigenvalue greater than 1 , explaining 17.1%, 15.6%, 11.1%, 8.9% and 7.4% of the variance , whereas some patients do not present any symptomatic cluster (n = 29). The most prevalent combination was the one with the 5 factors (n = 54).In total, 218 subjects presented the “emotional instability”, “impulsivity” and the “sleep problems” factors were the main predictor of scores . These factors were also the main predictors for the WFIRS-Social functioning subscale . The “impulsivity”, the “inattention/disorganization” and the “sleep problems” factors were the main predictors of the WFIRS-Work and WFIRS-Daily skills subscales . Regarding the WFIRS-Self-concept subscale, the main predictors consisted in “emotional instability”, “overactivation” and the “inattention/disorganization” factors . For the WFIRS-Risky behaviors subscale the factors “emotional instability”, “inattention/disorganization” and “overactivation” were the main predictors .A multiple regression analysis was conducted to partial out the specific contribution of the five symptomatic clusters to the functional impairment of adults with ADHD. The five obtained factors were simultaneously entered as predictors in the model. Results are presented in Table emotional instability”, the ‘impulsivity” and the “sleep problems” factors, the CTQ-SF accounted respectively for 10.6%, 4.5% and 2.4% of the variance .To partial out the specific contribution of childhood maltreatment to the obtained factors  a simple regression was conducted using the CTQ-SF as a predictor variable. Results are presented in Table emotional instability”, refers to emotional fluctuations and the vulnerability to negative emotions. The second cluster, i.e., “impulsivity”, refers to the excessive emotional reactions in response to both positive and negative emotional situations and is in line with the concept of emotional impulsivity described by Barkley and Fischer [overactivation”, encompasses several strengths of functioning of adults with ADHD, including hyperthymic temperament traits, racing thoughts and creativity. This factor is akin to the positive emotionality factor recently highlighted in the literature [inattention/disorganization” and “sleep problems”, refer to attention and cognitive problems and sleep difficulties.This study aimed at identifying the clinical patterns of ED in adults with ADHD, through the use of self-reported questionnaires assessing several psychological dimensions potentially related to ED. Overall, our results suggest that the clinical presentation of adults with ADHD is broader than the classic symptomatic triad comprising inattention and hyperactivity-impulsivity . Indeed, Fischer  and Bark Fischer . The thiterature . The othinattention/disorganization” concerns 61.38% of our sample of adults with ADHD. This number is similar to that referring to the “emotional instability” and/or “impulsivity” factors, as 67.52% of our sample presented with at least one of these symptoms. Interestingly, some authors suggest that considering two ADHD phenotypes (i.e. an inattention presentation and a combined inattention and dysregulated emotions presentation) may be more accurate than the classic three phenotypes  [Our results are in line with those of prior studies using factor analysis which suggest that inattention, associated with executive dysfunction, is a robust feature of ADHD , 92, 93.tations) , 75. Regtations) , 90, 94,emotional instability” or the “impulsivity” factor alone. This can be explained by the temporal model of emotions symptoms in ADHD [emotional instability” and “impulsivity” factors (82.17%), while the inattentive ADHD presentation concerned only 14.73% of those patients. This suggests that different mechanisms may underpin the experience of ED in the combined and the inattentive presentation of ADHD. Furthermore, consistent with a number of studies, the ED factors contributed to most functional difficulties of adults with ADHD, i.e. behavioral, academic and social impairment [Regarding the patterns of ED found in adult ADHD, consistent with the study led by Weibel et al. , we foun in ADHD . In this in ADHD  posit th in ADHD . In cont in ADHD . In line in ADHD , we founpairment , 95, 96.pairment  also idepairment , similarpairment , 97, 98.overactivation” was positively associated with increased self-esteem and did not contribute to any functional difficulties. Importantly, there is a growing number of studies focusing on the strengths of the ADHD functioning, shifting therefore from the deficit-focus view of ADHD [overactivation” factor. Interestingly, in a qualitative study on the ADHD strengths, Sedgwick et al. [overactivation” factor. Individuals presenting with hyperthymic temperament display exuberance, tirelessness, sensation seeking and a high level of energy [overactivation” factor, reported more positive aspects of ADHD and fewer emotional difficulties, which are involved in the functional impairment associated with ADHD.In contrast to ED, the factor “ of ADHD –101. Crek et al. . Furtherk et al. , consistf energy . Althougf energy , 72, it f energy . Indeed,f energy , such asf energy , 105. Mof energy . OverallWe acknowledge some limitations to this study. First, given the naturalistic approach used, the adults with ADHD recruited presented with different comorbidities, which may have influenced the factor analysis. Secondly, since our sample of adults with ADHD was recruited in a clinical setting, it included people who sought care and were likely to present with significant functional impairment,. Hence, participants probably experienced more severe ADHD and ED symptoms than the general adult ADHD population, limiting the generalization of our results to the general ADHD. For instance, the overactivation features might be more prominent in the general ADHD community, including adults who have a satisfactory quality of life. A replication of this study in the general population is warranted to capture a broader spectrum of ADHD and ED symptoms severity and to confirm the distribution of symptomatic factors more generally. Third, given the sample size, we were unable to perform a factor analysis on the items of each questionnaire and used the global scale or subscales scores. An item-based analysis could yield a more accurate description of ADHD. Nevertheless, this study counted a total of 20 measures allowing a large scanning of the ADHD functioning. Future studies should investigate the relative importance of each symptom using a network analysis approach. As an example Silk et al.  used thiIn this study, we identified five symptomatic clusters that characterized adults with ADHD. Among them, ED appears as a central bi-factorial symptom, encompassing emotional lability and emotional impulsivity, contributing to greater functional impairment and partially explained by childhood maltreatment. However, adult ADHD cannot be reduced to a collection of clinical impairing symptoms, given that several features can be characteristic of high functioning ADHD and be involved in compensatory strategies used to overcome difficulties. In sum, there are relevant features beyond the classical symptomatic triad that may have significant implications for ADHD identification in adults. However, beyond functional impairment, ADHD may also be characterized by functional strengths.


Social deficits are among the most important functional impairments in attention-deficit/hyperactivity disorder (ADHD). However, the relationship between social impairment and ADHD core symptoms as well as the underlying cerebral blood flow (CBF) characteristics remain unclear.P < 0.01, and cluster-level P < 0.05).A total of 62 ADHD subjects with social deficits (ADHD + SD), 100 ADHD subjects without social deficits (ADHD-SD) and 81 age-matched typically developing controls (TDC) were enrolled. We first examined the correlation between the Social Responsiveness Scale (SRS-1) and ADHD core symptoms  and then explored categorical and dimensional ADHD-related regional CBF by arterial spin labeling (ASL). For the categorical analysis, a voxel-based comparison of CBF maps between the ADHD + SD, ADHD-SD, and TDC groups was performed. For the dimensional analysis, the whole-brain voxel-wise correlation between CBF and ADHD symptoms  was evaluated in three groups. Finally, correlations between the SRS-1 and ADHD-related regional CBF were investigated. We applied Gaussian random field (GRF) for the correction of multiple comparisons in imaging results (voxel-level The clinical characteristics analysis showed that social deficits positively correlated with ADHD core symptoms, especially in social communication and autistic mannerisms domains. In the categorical analysis, we found that CBF in the left middle/inferior temporal gyrus in ADHD groups was higher than TDCs and was negatively correlated with the social motivation scores. Moreover, in dimensional analysis, we found that CBF in the left middle frontal gyrus was negatively correlated with the inattention scores, SRS total scores and autistic mannerisms scores in ADHD + SD subjects.The present study shows that inattention, hyperactivity, and impulsivity may be responsible for the occurrence of social deficits in ADHD, with autistic traits being another significant contributing factor. Additionally, CBF in the left middle/inferior temporal gyrus and the left middle frontal gyrus might represent the corresponding physiological mechanisms underlying social deficits in ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset neurodevelopmental disorder characterized by age-inappropriate inattention, hyperactivity, and impulsivity . It is oPrevious research suggests that social impairment in ADHD subjects may exist in conjunction with and apart from the disorder’s core symptoms . InattenAccumulating neuroimaging studies neuroimaging studies have begun to map the social brain . HoweverCerebral blood flow (CBF), as a physiological parameter closely related to cerebral metabolism, has received much attention in recent years. The more active and connected neuronal regions of the brain tend to have greater metabolic demands, resulting in increased cerebral perfusion . ArteriaThe results of previous CBF studies in ADHD have revealed that patients with ADHD are hyperperfused in the left caudate nucleus and in the frontal and parietal regions . HoweverGiven that social impairment may underlie the development of ADHD symptoms, investigation of the relationships between social profiles and brain perfusion would deepen our understanding of the pathology of ADHD. In this study, we sought to determine whether the social deficits in ADHD are connected to ADHD characteristics and to investigate the corresponding physiological mechanisms from quantitative and categorical perspectives. We assumed that the core ADHD symptoms, namely, inattention, hyperactivity and impulsivity, might be correlated with social deficits and that there might be characteristic cerebral blood perfusion features that underlie.A total of 162 medication-naïve children and adolescents with clinically diagnosed ADHD based on the DSM-5 were recruited from the outpatient department of Peking University Sixth Hospital  from July 2015 to January 2019. Another 81 age-matched TDCs were recruited from schools or nearby communities using recruitment advertisements. The clinical diagnosis was first made by a senior child and adolescent psychiatrist based on the diagnostic criteria of the DSM-5 and was then confirmed by a semistructured interview with the parents and child, performed using the Kiddie Schedule for Affective Disorder and Schizophrenia for School Age Children-Present and Lifetime Version (K-SADS-PL) . ParticiTo allow evaluation of the severity of symptoms, the ADHD Rating Scale-IV (ADHD-RS-IV)  and sociAll MRI images were acquired on a GE Discovery 3.0 T MR750 system at the Center for Neuroimaging in Peking University Sixth Hospital. Participants were instructed to relax with their eyes closed, stay still, and think of nothing in particular without falling asleep during the scans. The parameters of the T1 image were as follows: repetition time (TR) = 6.66 ms; echo time (TE) = 2.93 ms; inversion time (TI) = 450 ms; flip angle (FA) = 8°; field of view (FOV) = 256 × 256 mm; matrix = 256 × 256; slice thickness = 1.0 mm with no gap, 180 sagittal slices. Resting-state perfusion imaging was performed using a pseudo-continuous ASL (pcASL) sequence with a 3D fast spin-echo acquisition and background suppression: TR/TE = 4781/11.12 ms; post-label delays = 1525 ms; spiral in readout of 12 arms with 512 sample points; FOV = 220 mm × 220 mm; matrix = 128 × 128; FA = 111°; slice thickness = 3 mm, no gap; 45 axial slices; and the number of excitations = 3.1 First, the quantitative CBF images were calculated by using a single-compartment kinetic model because the pCASL and M0 images were acquired separately. Then, the T1 image was coregistered to the perfusion image, which was aligned with the corresponding CBF image. The coregistered T1 image was segmented into gray matter, white matter, and cerebrospinal fluid. Next, the CBF map was normalized into the MNI space based on the transform parameters that were estimated during the segmentation of the T1 image and further resampled to 3 mm × 3 mm × 3 mm. Finally, the normalized CBF images were smoothed with 4 mm FWHM. CBF analysis was conducted within the gray matter mask without the cerebellum (N voxel = 45.381). For each participant, the spatially normalized CBF images were transformed to z-scores by subtracting the mean and then dividing by the standard deviation across all voxels in the gray matter mask for further statistical analysis.The preprocessing of ASL data was performed using the ASLtbx software  and SPM1post-hoc analysis. A chi-squared test was used to compare the distribution of categorical data between groups. Spearman’s correlation analysis was conducted to examine the correlation between ADHD symptoms and social responsiveness profiles with age and gender as covariates. The significance level was set at P < 0.05.The demographic and clinical data were compared using SPSS 26.0. For normally distributed variables with homogeneity of variance, ANOVA was used, with the Bonferroni test used for P < 0.01, and cluster-level P < 0.05)  was examined in three groups. For each significant cluster from the above analysis, we calculated the relationship between its CBF and the social profiles. We first extracted the average CBF value of a given cluster and then calculated the partial correlation between each SRS measurement and CBF value in the corresponding groups. All image analyses involved using age, gender and handedness as covariates. For all these imaging results, we applied Gaussian random field (GRF) for the correction of multiple comparisons (voxel-level  < 0.05) .p > 0.05). Moreover, there were no differences between the ADHD + SD and ADHD-SD subjects in ADHD-RS-IV scores. However, the three groups did differ in gender  and IQ ; the percentage of females and the IQ in the TDC children were higher than those in the ADHD + SD and ADHD-SD children, but there were no statistically significant differences between the ADHD + SD and ADHD-SD subjects.A total of 62 ADHD + SD subjects, 100 ADHD-SD subjects and 81 TDC subjects were included in this analysis see . There wr = 0.366, P = 0.004), as well as the social communication scores  and autistic mannerisms scores . Furthermore, the ADHD-RS-IV IA scores showed a significant positive correlation with the SRS-1 total scores . Similarly, the ADHD-RS-IV HI scores also displayed a significant positive correlation with the SRS-1 total scores , as well as the social communication scores  and autistic mannerisms scores . In the ADHD-SD group, the ADHD-RS-IV total scores were significantly positively correlated with the autistic mannerisms scores , while the ADHD-RS-IV HI scores were significantly negatively correlated with the social motivation scores . The TDC participants demonstrated a closer correlation between ADHD core symptoms and social measures. However, we should take it more conservatively because SRS data was available for only 37 TDCs.The correlation between ADHD symptom scores and social impairment symptom scores differed among the subjects in the three groups see . In the post-hoc analysis revealed that the ADHD + SD and ADHD-SD participants did not exhibit any significant difference in this particular cluster . Further ster see .r = −0.521, GRF corrected voxel P < 0.01), negatively correlated with the ADHD-RS-IV HI scores in the left temporal pole  and positively related to the ADHD-RS-IV HI scores in the right precentral gyrus . In the ADHD-SD group, the CBF value was positively correlated with the ADHD-RS-IV IA scores in the right superior temporal gyrus . No significant correlations were found in the TDC group  in the ADHD + SD subjects. The partial correlation analysis in the ADHD-SD and TDC subjects was not significant.The significant cluster in the categorical analysis, the left middle/inferior temporal gyrus, showed that the CBF value was negatively correlated with the social motivation scores  and the autistic mannerisms scores  in the ADHD + SD subjects. No significant correlations were found between the CBF values in other regions and social measures in the ADHD-SD and TDC subjects.The significant cluster in dimensional analysis, the left middle frontal gyrus, was negatively correlated with the SRS total scores  and is the “motor” aspect of social behavior . There iThe correlation between ADHD core symptoms and ASD traits suggested that the ASD characteristics of ADHD may be another important reason why ADHD children are prone to having social deficits , as 15–2In our categorical analysis, we found that the CBF in the left middle/inferior temporal gyrus had no significant correlation with ADHD symptoms, but the CBF in this region was significantly higher in the ADHD subjects. This is consistent with previous studies, which have confirmed increased activity in the left middle/inferior temporal gyrus in ADHD subjects by fMRI . MoreoveBased on our dimensional analysis, we found that social deficits were associated with IA-related regions and that the lower the CBF in the left middle frontal gyrus, the more severe the attention deficit, social impairments and autistic behavior. The involvement of the left middle frontal gyrus in both IA and social deficits is not surprising given that prior studies have implicated this region as a part of the multiple demand system . The finThe study has several limitations. First, the SRS was designed to be a social functioning measure scale for ASD, which may explain why the symptoms in ADHD are closely related to autistic traits in our findings. Moreover, there were only 37 cases with SRS scale results in the TDC group, which may have resulted in statistical false positives. We need to be more conservative about the relationship between ADHD and social deficit symptoms in the TDCs group.In summary, by interpreting our results, we demonstrated the relationships between core ADHD symptoms and social deficit symptoms and found that inattention, hyperactivity, and impulsivity may be responsible for the occurrence of social deficits in ADHD. In particular, we found that social deficits in ADHD may not entirely result from ADHD symptoms alone, and the existence of autistic traits was another significant contributing actor. Moreover, CBF in the left middle/inferior temporal gyrus was related to social motivation, and CBF in the left middle frontal gyrus was related to the SRS total scores and autistic mannerisms, which might represent the corresponding physiological mechanisms underlying the social deficits in ADHD.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 the Medical Ethics Committee of Peking University Sixth Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was obtained from the individual(s) and minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.KZ analyzed the data and finished the original manuscript draft. JY and XP collected the data. NH scanned the MRI. ZF and YZ organized the clinical information. QC and LY reviewed the methods and the whole manuscript. YW supervised the project. All authors contributed to the article and approved the submitted version.