Attention deficit hyperactivity disorder (ADHD) was considered to be a disorder with high heterogeneity, as various abnormalities were found across widespread brain regions in recent neuroimaging studies. However, remarkable individual variability of cortical structure and function may have partially contributed to these discrepant findings. In this work, we applied the Dense Individualized and Common Connectivity-Based Cortical Landmarks (DICCCOL) method to identify fine-granularity corresponding functional cortical regions across different subjects based on the shape of a white matter fiber bundle and measured functional connectivities between these cortical regions. Fiber bundle pattern and functional connectivity were compared between ADHD patients and normal controls in two independent samples. Interestingly, four neighboring DICCCOLs located close to the left parietooccipital area consistently exhibited discrepant fiber bundles in both datasets. The left precentral gyrus  and the right anterior cingulate gyrus  had the highest connection number among 78 pairs of abnormal functional connectivities with good cross-sample consistency. Furthermore, abnormal functional connectivities were significantly correlated with ADHD symptoms. Our studies revealed novel fine-granularity structural and functional alterations in ADHD. Attention deficit hyperactivity disorder (ADHD) is one of the most common neurobehavioral psychiatric disorders of childhood, and its prevalence rates based on teacher reports were an estimated 5.47% . The typThe scattered brain areas that exhibit neuroactivities at resting state are called default mode network (DMN), which includes medial prefrontal cortex, precuneus/posterior cingulate cortex, lateral parietal cortex, and inferior temporal lobule. Previous studies have shown that the synchronization of neural activity , pointed out that one of the future directions would be big sample data, and acquiring large samples needs multisite data sharing. Big data from a single site may produce statistically significant but trivial results, whereas no result is believable until it has been replicated in multiple independent datasets . Big datTherefore, in the ADHD fMRI study, under the demand of multicenter data sharing, considering the individual brain differences, a method to define brain regions with fine-granularity and better structural/functional consistency across brains is warranted.The Dense Individualized and Common Connectivity-based Cortical Landmarks DICCCOL;  system dThus, in the current study, we adopted the DICCCOL system in two independent ADHD datasets. We hypothesized that children with ADHD (1) exhibit abnormal fiber bundle, (2) exhibit abnormal resting functional connectivities, and (3) exhibit consistent abnormalities across independent datasets.This study includes two independent datasets. These two datasets were collected for different purposes and using different scan parameters. Previous analysis of these datasets has been published elsewhere separately . Only diThe ADHD Rating Scale-IV forms were collected from the parents of ADHD children to assess the severity of ADHD symptoms. Written informed consent was obtained from each parent, and each child agreed to participate. The Institutional Review Board at the Health Center of Peking University approved this study.b = 1,000 s/mm2, thickness = 2.5 mm, slice spacing = 0 mm, and acquisition matrix = 128 × 128. As for dataset 2, the diffusion-weighted MRI images are acquired by following parameters: 47 axial slices, TR = 6,900 ms, TE = 104 ms, flip angle = 90°, 64 diffusion directions, b = 1,000 s/mm2, thickness = 2.5 mm, slice spacing = 2.5 mm, and acquisition matrix = 128 × 128. The RS-fMRI images of these two datasets shared similar parameters. They were acquired using an echo-planar imaging sequence with 33 axial slices, TR = 2,000 ms, TE = 30 ms, flip angle = 90°, slice thickness = 3 mm, slice spacing = 3.6 mm, in-plane resolution = 64 × 64, and 240 volumes.Both two datasets with multimodal diffusion-weighted/RS-fMRI data are acquired in a SIEMENS TRIO 3T scanner at the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University. The diffusion-weighted MRI images of dataset 1 are acquired using the following parameters: 49 axial slices, repetition time (TR) = 7,200 ms, echo time (TE) = 104 ms, flip angle = 90°, 64 diffusion directions, 1. The fractional anisotropy threshold was 0.2, and the minimum fiber length was 20 mm. Brain tissue segmentation was conducted on the diffusion-weighted MRI data directly , abnormal DICCCOL ROIs in each dataset were identified. Then, we investigated whether there are overlapped results between the two datasets.In the discussed steps, the shape of each fiber bundle will be quantified as a “trace-map.” In this way, the morphological differences between fiber bundles can be quantified as “trace-map distance.” The specific algorithm can be found in the previous publication . AccordiBecause the DICCCOL method is based on the “Connectional Fingerprint Concept” , which sp < 0.05). Again, by overlapping the result of two datasets, we got a relatively consistent abnormal functional connectivity map.After the co-registration between fMRI and diffusion-weighted MRI data, we extracted the RS-fMRI BOLD signals of each DICCCOL ROI for each participant. Functional connectivities were calculated for each pair of DICCCOL ROIs. After Fisher’s r-to-z transform, we conducted between-group comparison (To study the relationship between ADHD symptoms (ADHD-RS score) and abnormal functional connectivity with good cross-sample universality, we applied generalized linear regression, with covariances including age/sex/IQ.t-test was used to compare means. Categorical variables were compared with the chi-square test. The differences in sex and age were not significant.The information for the demographics/clinical data of the participants is summarized in As expected, in the ADHD group, certain DICCCOL ROIs displayed notably higher trace-map distance compared with the control group, which indicated that the fiber bundle patterns at these locations have higher variability. Four DICCCOL ROIs  among them were shared by both datasets. The positions and trace map distance of these four DICCCOL ROIs, as well as the shape of the fiber bundles, are shown in Supplementary Materials.The 78 consistent abnormal functional connectivities in the ADHD group across datasets are shown in Three of the 78 overlapping functional connectivities (Z score) significantly predicted the ADHD parent rating scale score. The result is shown in In the current study, after the DICCCOL prediction procedure, we found that ADHD patients showed notably discrepant fiber bundle patterns on a number of DICCCOLs, and four of them were shared by both datasets. As shown in The DICCCOL method established 358 individual landmarks on each participant’s brain. DICCCOLs with the same number were considered to have good functional correspondence among different participants. A meta-analysis labeled each DICCCOL with functional meaning by registering them to 1,110 previous task-fMRI research . The resWe found that DICCCOL nos. 34, 37, 38, and 44, located near the junction of the left parietal and occipital lobe, exhibited abnormal fiber bundle structure in the ADHD group. These four DICCCOLs were very close in spatial location, and there were no other DICCCOLs between them. Therefore, we speculated that these four DICCCOLs might be small subregions of a larger functional brain region. These abnormalities of the four adjacent landmarks may indicate the structural and, thus, functional  abnormalBy applying functional connectivity analysis between DICCCOL ROIs with no difference in WM fiber bundle morphology, we obtained 1,682 functional connectivities with between-group differences in dataset 1 and 3,357 in dataset 2. Seventy-eight of them exhibited good consistency across datasets. Interestingly, we found that a quarter (19 of 78) of these abnormal functional connectivities were associated with DICCCOL ROI nos. 175 and 321. Therefore, these two DICCCOLs with abnormal functional connectivities, which are consistently shown in two separate datasets, may play an important role in the pathogenesis of ADHD.According to the previous meta-analysis , DICCCOLDICCCOL 321 located in the right anterior cingulate gyrus is related to cognition and emotion . During The functional connectivities related to ADHD symptoms were mostly between the default network components and other brain networks, such as the right precuneus and the right ACC (DICCCOL 43–324) and left precuneus and right superior frontal gyrus (DICCCOL 60–204), which reflected the abnormal function of DMN and other networks in resting state. In addition, functional connectivity between the bilateral precentral gyrus (DICCCOL 161–184) was associated with hyperactivity impulse scores. Together with the discussed findings of abnormal functional connectivity in the precentral gyrus (DICCCOL 175), this finding highlights the significance of abnormal activity of the precentral gyrus in patients with ADHD under resting state. In contrast, there is no functional connectivities related to the inattention score of ADHD-RS, which may be related to the low demand on attention level in the resting state. This issue could be verified in the future by combining task and resting-state scans.Method, because these abnormal DICCCOLs do not have good WM fiber bundle morphological similarity, we cannot arbitrarily determine their functional correspondence. This is the temporary limitation of the DICCCOL method and a critical issue to be overcome in the application of future disease research. (2) The “connectional fingerprint concept” (The current study has the following limitations: (1) we abandoned some abnormal DICCCOLs during fMRI analysis, so 322 DICCCOLs are not a perfect representation of the “whole-brain;” however, the brain activity with more ADHD characteristics may be reflected in these DICCCOLs. As mentioned in concept”  is a theIn this study, we used the DICCCOL method and replicated some findings consistent with previous studies on two independent ADHD samples, and some unique findings were also found: there were morphological abnormalities in the WM tracts in the left posterior cortex of the DMN in children with ADHD; in the resting state, the abnormal functional connectivities with good cross-sample consistency mainly involved attention, motion, emotion, and working memory (DICCCOL 175/321). This study provides a new possibility for multicenter and large sample ADHD study in the future. Although the physiological significance of the part of the DICCCOL landmarks is still unclear, Castellanos points out that effective biomarkers do not necessarily have a clear neurophysiological significance or explanation . From thThe 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 Institutional Review Board at the Health Center of Peking University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.YW, PW, and SZ: conception and design. PW, XJ, HC, SZ, XL, BY, and YW: analysis and interpretation. PW, QC, LS, and LL: data collection. PW and SZ writing the manuscript. XJ and QC: critical revision of the manuscript. PW, SZ, and XJ: statistical analysis. YW: overall responsibility. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This cross-sectional questionnaire study examined factors related to affiliate stigma among caregivers of children with attention-deficit/hyperactivity disorder (ADHD) and the association of affiliate stigma with caregivers’ unfavorable attitude toward ADHD and moderators. The affiliate stigma of 400 caregivers of children with ADHD was assessed using the Affiliate Stigma Scale. Caregivers’ and children’s factors related to affiliate stigma were examined using multiple regression analysis. Associations of affiliate stigma with caregivers’ unfavorable attitudes toward children’s diagnoses, pharmacotherapy, behavioral therapy, and biological explanations of the etiologies of ADHD were examined using logistic regression analysis. Female caregivers and those caring for girls with ADHD had higher levels of affiliate stigma than did male caregivers and those caring for boys. Higher education levels in caregivers and more severe inattention symptoms in children were associated with higher levels of affiliate stigma. A higher level of affiliate stigma was also significantly associated with unfavorable attitudes toward children’s diagnoses, pharmacotherapy and behavioral therapy, and etiological explanations for ADHD. Multiple factors of caregivers and children were related to affiliate stigma in caregivers of children with ADHD. Affiliate stigma is significantly associated with caregivers’ unfavorable attitude toward ADHD. Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a lifetime prevalence of 10.1% according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria . AlthougStudies have examined ADHD-related public stigma, self-stigma, and courtesy stigma and their negative effects. A high level of perceived public stigma toward ADHD can predict low willingness to receive intervention  and low Affiliate stigma in caregivers of people with mental illness develops through perceiving and internalizing public stigma toward caregivers . CaregivA growing body of research has investigated affiliate stigma in family caregivers of people with schizophrenia, depression, or affective disorders . These sCultural background affects levels of stigmatization toward ADHD . People Rather than focusing on courtesy stigma addressing public views toward caregivers, the present study focused on affiliate stigma in caregivers of children with ADHD. Based on sociocultural background, this cross-sectional questionnaire study examined the level of affiliate stigma, its related sociodemographic and illness factors, and its association with unfavorable attitudes toward ADHD and moderators among family caregivers of children with ADHD in Taiwan. Identification of factors that contribute to developing affiliate stigma is crucial in promoting interventions and prevention. A review of studies demonstrated that results are mixed regarding associations between affiliate stigma and sociodemographic characteristics  of caregivers and affiliated people with mental illnesses (mainly schizophrenia and affective disorders) . In addiThe present study also examined the association of affiliate stigma with unfavorable attitude toward the diagnosis, treatment, and etiological explanation of ADHD and moderators among family caregivers of children with ADHD in Taiwan. Although neurocognitive etiologies and treatment for ADHD have been proposed , caregivCaregivers of children who were aged 18 years or younger and had received a diagnosis of ADHD according to DSM-5 criteria ,33—were The Affiliate Stigma Scale (ASS) is a self-rated 22-item questionnaire measuring caregivers’ internalization of stigma toward family members’ mental illness . In accoWe invited the caregivers to answer four items on the research questionnaire to evaluate caregivers’ attitudes toward their children’s diagnoses, pharmacotherapy and behavioral therapy, and biological explanation of ADHD etiology on a 4-point scale. Given that the participants were recruited from outpatient clinics and were supposed to have a higher understanding of ADHD than caregivers of children with ADHD who did not visit outpatient clinics for ADHD problems, the present study classified caregivers who rated items with 1 (strongly unacceptable), 2 (mildly unacceptable), and 3 (mildly acceptable) as having unfavorable and those who rated items with 4 (strongly acceptable) as having favorable attitudes, respectively, toward diagnosis, pharmacotherapy, psychotherapy, and etiological explanations of their children’s ADHD.The short, Chinese version of SNAP-IV was used to assess caregiver-reported severity of ADHD symptoms exhibited in the preceding month. This version comprises 26 items encompassing the core DSM-derived ADHD subscales of inattention, hyperactivity and impulsivity, and oppositional symptoms of oppositional defiant disorder ,33. EachCaregivers completed a questionnaire for collecting their sex , age (years), years of education completed, marital status , occupational socioeconomic status (SES), and frequency of attending religious activities . The caregivers could request assistance with any problems they encountered in completing the questionnaire. According to marriage status, participants were grouped into those who were married and those who were divorced or separated. Occupational SES was assessed using the Close-Ended Questionnaire of the Occupational Survey (CEQ-OS) , which cp value of less than 0.05 was considered significant. The associations of caregiver affiliate stigma with unfavorable attitudes toward children’s diagnoses, pharmacotherapy and behavioral therapy, and biological etiologies of ADHD were examined using logistic regression analysis by controlling caregivers’ and children’s factors. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to indicate significance. We also used criteria proposed by Baron and Kenny [Data analysis was performed using SPSS 24.0 . Affiliate stigma levels on the three domains of the ASS were calculated. Caregivers’ and children’s factors related to affiliate stigma on the ASS were examined using multiple regression analysis. A two-tailed nd Kenny  to examiThe study procedures were performed in accordance with the Declaration of Helsinki. The IRBs of Kaohsiung Medical University Hospital and Chang Gung Memorial Hospital, Kaohsiung Medical Center approved the study. All participants were briefed on the study and provided written informed consent before completing research questionnaires.t = 23.22, p < 0.001) and behavior domains . The ASS score in the cognition domain was significantly higher than that in the behavior domain . Scores in the three ASS domains were highly correlated with each other (Pearson’s correlation r = 0.679–0.869). Therefore, the total ASS score was used to represent the level of affiliate stigma in caregivers of children with ADHD in further statistical analyses.Because caregivers’ education level and children’s age were significantly associated with unfavorable attitude toward ADHD diagnosis, the moderating effects of caregivers’ education level and children’s age on the association between affiliate stigma and unfavorable attitudes toward ADHD diagnosis were examined. The interaction between affiliate stigma and caregiver’s education level  and between affiliate stigma and children’s age  were not associated with unfavorable attitude toward ADHD diagnosis. The results did not support the moderating effect of caregiver’s education level and children’s age.In this study, although the three domains of affiliate stigma were highly correlated with each other, affiliate stigma in the affect domain was significantly higher than that in the cognition and behavior domains. Female sex and higher education level for caregivers and female sex and more severe inattention symptoms in children were significantly associated with higher levels of affiliate stigma. Affiliate stigma increased with caregivers’ unfavorable attitudes toward children’s diagnoses, pharmacological and behavioral therapy, and etiological explanations of ADHD. Sociodemographic factors and ADHD symptoms did not moderate the association between affiliate stigma and caregivers’ unfavorable attitudes toward ADHD.Affiliate stigma consists of three interrelated components: cognition, affect, and behavioral responses . BecauseIn this study, female sex and education level were positively associated with affiliate stigma in caregivers. Studies on caregivers of people with schizophrenia, affective disorders, or intellectual disabilities did not observe a significant association of caregivers’ sex and education level with affiliate stigma ,42,43,44Affiliated children’s female sex and inattention symptom severity were significantly associated with higher affiliate stigma levels. Results of video studies on the sex difference in stigma toward ADHD are mixed. Pescosolido et al. revealed that participants were more likely to avoid male children with ADHD , whereasCharbonnier et al. demonstrated that children’s ADHD symptoms were positively associated with affiliate stigma, which was positively associated with mothers’ distress . HoweverAffiliate stigma was positively associated with caregivers’ unfavorable attitudes toward children’s diagnoses, pharmacological and behavioral therapy, and etiological explanations of ADHD. A study also demonstrated that parental attitudes toward ADHD treatment were associated with susceptibility to ADHD stigma, ADHD knowledge, and misconceptions . Family The present study has several limitations. First, the cross-sectional design limited the possibility of determining a causal relationship of affiliate stigma with unfavorable attitude toward ADHD. In addition to the contribution of affiliate stigma to developing unfavorable attitudes toward ADHD, unfavorable attitude toward pharmacotherapy and behavioral therapy for ADHD may delay opportunities for treatment for children with ADHD and worsen their inattention symptoms, thus deepening caregivers’ affiliate stigma. Second, data for both affiliate stigma and children’s ADHD symptoms were reported by caregivers. The single data source may have resulted in common-method variance. Third, the present study did not examine the roles of multidimensional psychosocial factors, such as social support or health belief, in affiliate stigma or the association between affiliate stigma and caregivers’ unfavorable attitudes toward ADHD.Based on the results of the present study, we recommend that health care professionals view affiliate stigma as a critical topic that warrants vigorous evaluation and intervention. Affiliate stigma in family caregivers of children with ADHD results from interactions among multiple ecological systems. Children with ADHD and their caregivers may benefit from “ecologically sensitive” treatment in which children, caregivers, social environments, and broader political and cultural contexts that shape children’s behaviors are carefully investigated . In partMultiple factors of caregivers and children were related to affiliate stigma in caregivers of children with ADHD. Prevention and intervention programs for reducing affiliate stigma in caregivers of children with ADHD should take these related factors into consideration. Moreover, affiliate stigma is significantly associated with caregivers’ unfavorable attitude toward children’s diagnoses, pharmacotherapy and behavioral therapy, and etiological explanations for ADHD. The result supported that reducing affiliate stigma is important for treatment in ADHD.


Background: Few studies in clinical attachment research to date have examined children with an attention-deficit/hyperactivity disorder (ADHD) diagnosis. This is surprising for two reasons: first, there are a number of parallels between the behaviors of children with an insecure and disorganized attachment and the behaviors of children with an ADHD diagnosis. Second, secure attachment has a positive effect on the development of skills in areas in which children with ADHD demonstrate problems . There are currently no findings on whether or not and how insecure and disorganized attachment and ADHD affect children’s emotion recognition ability. Methods: This is a cross-sectional study, part exploratory and part hypothesis-driven in the context of basic research. A clinical sample of 5- to 10-year-old children with an ADHD diagnosis and their parents is to be compared to a non-clinical unaffected control group. Over a period of 3 years, 80 subjects and their parents are to be recruited in each group for participation in the study. Discussion: This study is the first to examine links between attachment, emotion recognition ability, and ADHD. It is also the first to include not just children with ADHD but also their mothers and fathers in its design. The findings should help reduce the research gap and generate more knowledge for family interventions in the case of ADHD. With an average prevalence of just over 5% , attentiIn early childhood, the child’s attachment qualities to his or her principal attachment figures develop according to how these figures deal with the child’s signals. If an attachment figure appropriately perceives everyday signals from the child, interprets them correctly, and acts promptly and suitably, Ainsworth, Bell, and Stayton  define tEmotional facial expressions provide necessary information for mutual social interactions. The ability to correctly decode those expressions is a key skill for the situational adjustment of social behavior. Even infants who are just a few months old are able to differentiate between happy, sad, and surprised faces and between different levels of intensity of feeling . Studiesp < 0.001) with a lower incidence of secure attachment representation and an increase in insecure and disorganized attachment representation on the part of the mothers.Children with ADHD can represent a major challenge in everyday family life right from infancy. There are now a number of empirical indications that the temperament and vitality of the infant play a part in the etiology of ADHD, in particular in the event of a mismatch with the temperament and vitality of the primary attachment figure ,25,26. PClear indications of a link between ADHD and attachment can be found in the previously separate research areas of ADHD, on the one hand, and attachment, on the other. Cassidy  has founA large number of studies have also shown that secure attachment has a positive effect on the development of skill sets in areas in which children with ADHD demonstrate problems: Maslin-Cole and Spieker  found thThe insecurely attached, on the other hand, display more problems in interpersonal relations than the securely attached ,41,42. FAlthough Salari et al.  found onAdults  and chilIn childhood development, the ability to recognize emotions is linked to the memory-based ability to develop expectations of how the other person will behave if his or her face is expressing emotion (for example happiness or anger). If the other person’s behavior matches the child’s expectations, this confirms the child’s mental representations of events. This, in turn, forms the basis for the more abstract and generalized internal working model (IWM) of attachment ,57,58,59The primary attachment figures of securely attached children allow their children to experience the full spectrum of emotions. Steele et al. , therefoOverall, it can be concluded in the light of the basic theory outlined and research findings, to date, that there are significant research desiderata regarding the links between ADHD and attachment, ADHD and emotion recognition, and attachment and emotion recognition. The study seeks to make a significant contribution to those research desiderata in order to better understand the situation of children with ADHD and their parents and generate more knowledge for family dynamics in the case of ADHD.What is the distribution of attachment representations of children with ADHD and their parents in comparison to clinically not affected children and their parents?Does the intergenerational transmission of attachment in the families of children with an ADHD diagnosis differ from that in families of clinically not affected children?Are there links between ADHD and emotion recognition ability?Are there links between the attachment representation of children and their emotion recognition ability?Are there complex links between the attachment representation of parents, the attachment representation of children, ADHD diagnoses, and emotion recognition ability?This is a cross-sectional study in the context of basic research, part exploratory and part hypothesis-driven. A clinical sample of children with an ADHD diagnosis and their parents is compared to a clinical unaffected control sample.n = 80) who are being treated as in-patients or day-care patients at the Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy at the University Clinic of Cologne/Germany. The study also includes the children’s parents. The non-clinical, parallelized control sample (n = 80) are to be recruited from schools in Siegen, Bonn, and Cologne.The clinical sample comprises 5–10-year-old ADHD children  or DSM-5 ;○Children in the control group: no infant or adolescent psychiatric diagnosis (acute or lifetime);○Written consent to participate in the study after an explanation of the study to the parents and the child;○Ages: 5–10;○A good command of the German language.Inclusion criteria:○IQ < 70;○Additional child or adolescent psychiatric diagnosis;○Serious neurological or somatic diseases;○Other disorders relating to perception, cognition, or affect that would impede or prevent a neuropsychological assessment;○Persons taking medication affecting the CNS (with the exception of methylphenidate);○Single-parent family.Exclusion criteria:The study is to be completed within a period of 3 years from the start of data collection. The aim is to complete data collection as far as possible after the first 30 months. The final 6 months are for data processing, statistical analyses, and reporting. Statistical analysis is to be carried out by the Chair of Developmental Science and Special Education at the University of Siegen.Inpatients will be diagnosed within the first three weeks of treatment. The time for conducting the attachment inventories is c. 40 min per person . A time of 30 min has been estimated for the neuropsychological assessment of the children. Children who are being treated with methylphenidate will be assessed in the morning before taking their medication . The control group is being recruited from participating schools in Siegen, Bonn, and Cologne. The time estimated for diagnosis and neuropsychological testing corresponds to that for the test group. Sociodemographic data will also be collected, and intelligence testing carried out for the child at the assessment appointment. The time for collecting all data, including breaks for the families, is c. 3 h. As an incentive, all test persons receive a cinema voucher worth EUR 15.All questionnaires have been validated or have been used in other studies and published in the literature.Data will be collected on nationality, socioeconomic status, family circumstances and the school career of the subjects. These details are to be supplemented with questions on the risk exposure of the family.The Wechsler Intelligence Scale for Children-IV (WISC-IV)  is to beDiagnostic checklist for hyperkinetic disorders (DCL-HKS)Diagnostic checklist for anxiety disorders (DCL-ANG)Diagnostic checklist for depressive disorders (DCL-DES)External assessment questionnaire for hyperkinetic disorders (FBB-HKS)External assessment questionnaire anxiety disorders (FBB-ANG)External assessment questionnaire for depressive disorders (FBB-DES)The initial diagnosis is by the attending child and adolescent psychiatrists. Diagnostic checklists (DCL) and external assessment questionnaires (FBB) from the ICD-10 and DSM-IV classification of mental and behavioral disorders in children and adolescents (DI-SYPS-KJ)  will alsThe Child Behavior Checklist (CBCL/4-18) will be used to check externalizing and internalizing behavioral abnormalities .Determining attachment representation at the various different ages is methodologically highly complex as it involves age-specific inventories requiring special training. All researchers on the study have successfully completed the required training at certified institutions. The attachment inventories conducted will be evaluated exclusively by reliable colleagues from other institutes who are not involved in the study itself.Attachment is to be diagnosed for participating children using the Geschichtenergänzungsverfahren (GEV-B) procedure . In termThe adult attachment projective (AAP)  is to beThe study uses the Diagnostic Analysis of Nonverbal Accuracy 2 (DANVA-2) . This is2 = 0.04 in the study by Kats-Gold et al. [2 = 0.81 in the study by Cadesky et al. [n = 123  [As the questions outlined have never before been investigated at this level of complexity, the literature only provides a limited indication of the scale of effects to be expected. The optimum sample size is calculated on the basis of the most complex question, the link between attachment representation and emotion recognition ability in children with ADHD. Expected effect sizes can most usefully be calculated on the basis of literature on the links between ADHD and emotion recognition. These effects range from a partial etad et al.  to eta2 y et al. , i.e., tseldorf) . As the z-values) where necessary, using the average and standard deviation from the non-clinical sample. The hypotheses on difference to be assessed will be explored using group comparisons. Parametric procedures will be used where data dignity allows; in other cases, appropriate non-parametric procedures will be employed. Correlation hypotheses are to be tested using AN(C)OVAS or multi-factor regression analyses. To improve the classification and comparability of results, the appropriate effect sizes for the procedures used are also to be calculated.Data is to be collected using standardized and tested instruments. A folder of findings for each participant in the study is to provide quick and easy access to the relevant findings. Data is primarily to be evaluated using the software package SPSS 26  at the Chair of Developmental Science and Special Education at the University of Siegen. To this end, the raw test results will be transformed into standard values  and the same number in the control group .One strength of the study is the sample size. If the intended case number is achieved, a total of 480 persons will be studied. Another innovation is that the study includes not just children with ADHD but also their mothers and fathers in its design. For the first time, it will, therefore, be possible to describe family dynamics from a range of perspectives on the basis of data and evaluate those dynamics in comparison with unaffected families. On the significance of fathers in particular, no data are available, to date, in the area outlined. Another strength is that the study uses questionnaires to check for other child and adolescent psychiatric clinical pictures in addition to ADHD.The limitations include the expected proportions of the different sexes. In clinical studies, it can be assumed the ratio of girls to boys will be 1:9. It will not be possible either to influence the proportion of F90.0 (attention-deficit hyperactivity disorder) to F90.1 (hyperkinetic conduct disorder) diagnoses in accordance with ICD-10. This is significant, as it is to be assumed that these are two distinctive clinical pictures that should not be combined in one group for statistical purposes ,71. WhilTo conclude, the study coordinators hope, on the one hand, to reduce the gap in the research in the areas outlined. They also believe that the findings obtained will contribute to advisory and psychotherapeutic work with children with an ADHD diagnosis and their parents.


Alterations of tactile processing have long been identified in autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, the extent to which these alterations are disorder-specific, rather than disorder-general, and how they relate to the core symptoms of each disorder, remains unclear. We measured and compared tactile detection, discrimination, and order judgment thresholds between a large sample of children with ASD, ADHD, ASD + ADHD combined and typically developing controls. The pattern of results suggested that while difficulties with tactile detection and order judgement were more common in children with ADHD, difficulties with tactile discrimination were more common in children with ASD. Interestingly, in our subsequent correlation analyses between tactile perception and disorder-specific clinical symptoms, tactile detection and order judgment correlated exclusively with the core symptoms of ADHD, while tactile discrimination correlated exclusively with the symptoms of ASD. When taken together, these results suggest that disorder-specific alterations of lower-level sensory processes exist and are specifically related to higher-level clinical symptoms of each disorder. He et al. assessed different parameters of tactile processing in children diagnosed with autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). They found evidence for disorder-specific alterations of lower-level sensory processing, which can be related back to higher-level clinical symptoms. Infants receiving more tactile-kinesthetic stimulation from their caregivers are found to have better developmental outcomes, including greater weight gain, healthier sleep/wake cycles and accelerated motor development2. Over and above its effect on early physical development, tactile processing plays a critical role in social development3. Interpersonal “social touch” has a wide range of functions in early life4. Touch is critical to the formation of mother–infant attachments5, a relationship that provides one of the initial platforms from which gesture-based6 and verbal communication skills are first practiced and developed7. In early childhood, tactile perception affects the willingness of children to participate in rough-and-tumble play with peers8, an act which is indispensable to early social development9. Of course, in later adolescence and adulthood, tactile processing also becomes an important factor in the development of romantic interpersonal relationships10.The role that tactile processing plays throughout the human life course cannot be understated. Indeed, touch is the first sense to develop prenatally12, the extent to which these abnormalities are specific to each disorder , and how they relate to the core behavioral phenotypes of each disorder remain unclear. Indeed, this latter question has recently been highlighted as an outstanding question for further inquiry by many in the field13. Although earlier versions of the diagnostic statistical manual of mental disorders (DSM) prohibited the co-diagnosis of ASD and ADHD (ASD + ADHD), the publication of the DSM-514 has since permitted a clinical diagnosis of ASD + ADHD. This change in diagnostic practice has implications for how we characterize and treat each of these disorders, bringing forward the need for more formal investigations into the degree to which previously identified symptoms are specific and/or general to each disorder. In the context of sensory processing abnormalities in ASD and ADHD, it is possible that some or even all of the previously identified sensory abnormalities in one disorder were simply due to the pathophysiology of the other. Indeed, while elevated detection thresholds and wider temporal binding windows have been identified in ASD across a range of sensory domains19, findings have been mixed26, with many studies not controlling for ADHD symptomatology. Problematically, elevated detection thresholds and wider temporal binding windows have also been identified in ADHD, suggesting that the similar findings in ASD may actually be explained by co-occurring ADHD. Additionally, it is possible that sensory abnormalities that would not otherwise be present in isolated cases of either disorder are only present in individuals with a co-diagnosis of ASD + ADHD. Indeed, having ASD + ADHD could even mediate or moderate any existing sensory sensitivities specific to each disorder in isolation.While tactile processing abnormalities have been reported in both ASD and ADHD30. While informative in their own right28, these are limited in their ability to address the disorder-specificity of sensory and, more specifically, tactile abnormalities in ASD and ADHD, as well as whether and how they relate to the core symptoms of each disorder. For instance, many of the embedded questions assess an individual’s subjective emotional reaction to sensory stimuli , rather than provide an objective assessment of actual alterations in sensory processing. Moreover, while many questionnaires differentiate between sensory domains , they collectively do not differentiate between perceptual domains . This latter limitation is perhaps the most important. By failing to differentiate between perceptual domains, which represent distinct aspects of cortical processing, questionnaires can only capture sensory abnormalities in broad brush strokes and likely lack the resolution to distinguish disorder-specific alterations in the cortical processing of sensory information.Thus far, studies which have aimed to characterize tactile processing abnormalities in ASD and ADHD have typically relied on subjective self/caregiver report questionnaires31. With the aim to determine the extent to which abnormalities in tactile processing are specific to ASD and ADHD, we compared psychophysically derived tactile perceptual thresholds across a large sample of children (8–12 years of age) with ASD with no co-diagnosis of ADHD (n = 34), ADHD with no co-diagnosis of ASD (n = 104), combined ASD + ADHD (n = 99) and typically developing controls  detection thresholds were also those reported to have more problems with both hyper-responsivity and hypo-responsivity to sensory stimuli, as well as problems with social participation. When correlating measures from the tactile processing subdomains to the core clinical symptoms of each disorder, a remarkable pattern emerged. Our results showed that the disorder-specific tactile processing alterations identified in our initial group comparisons were also the metrics that were most commonly and specifically related to the core symptoms of each respective disorder. For instance, higher amplitude and frequency discrimination thresholds, which were more common to children with ASD (including ASD + ADHD), were exclusively related to problems with communication in ASD. Conversely, order judgement thresholds, which was more commonly elevated in children with ADHD (including ASD + ADHD), was exclusively related to problems with inattention in ADHD. As expounded below, our results provide an important description of tactile processing abnormalities in ASD and ADHD, showing that although children with ASD, ADHD, and ASD + ADHD do indeed have alterations in tactile processing, the perceptual domains in which those alterations occur, and how they relate to clinical symptomology, follow a disorder-specific pattern. The neurophysiological implications of our results are discussed, with specific reference to the potential cortical GABAergic processes that might be affected in these disorders.32. Generally speaking, lower thresholds imply greater sensitivity, while higher thresholds imply worse sensitivity. Through a battery of these protocols  an individual can accurately perceive and are relatively stable characteristics across timesee Fig. , sequentsee Fig. c, d, andsee Fig.  for tactF = 11.24, p < 0.001; η2p = 0.08; BF10 = 3.4907). There was also a main effect of group on median reaction times  = 7.91, p < 0.001; η2p = 0.06; BF10 = 2.7812), accuracy  = 7.13, p = 0.001) and the number of reversals  = 14.31, p < 0.001; η2p = 0.06; BF10 = 689,653.94). The ADHD (t(375) = 4.51, pTukey < 0.001; d = 0.60; BF10 = 2803.56) and ASD + ADHD (t(375) = 4.83, pTukey < .001; d = 0.70; BF10 = 16,112.13) groups had significantly higher detection thresholds compared to the typically developing control group, while the ASD group did not (t(375) = 1.62, pTukey = 0.367; d = 0.34; BF10 = 0.85). This pattern of group differences suggests difficulties with detecting tactile stimuli are more common to children with ADHD. While children in the ADHD and ASD + ADHD had higher detection thresholds than the typically developing control group, those with a diagnosis of ASD without a co-diagnosis of ADHD, did not. This finding is generally consistent with earlier work conducted both in our lab34 and others35. A point of difference in the current study, however, is that elevated detection thresholds were only identified in the ASD group with a co-diagnosis of ADHD.Results from group comparisons on performance outcomes of the detection protocol are shown in Fig. F = 5.79, p < 0.001; η2p = 0.04; BF10 = 14,610.86) and a non-significant trend towards a main effect of group on accuracy  = 2.39, p = 0.069; η2p = 0.02; BF10 = 3.40). There was no significant main effect of group on median reaction times  = 1.31, p = 0.269; η2p = 0.01; BF10 = 0.08), or the number of reversals  = 1.90, p = 0.128; η2p = 0.02; BF10 = 0.03). Post hoc comparisons found that while individuals in the ASD + ADHD (t(395) = 4.02, pTukey < 0.001; d = 0.70; BF10 = 258.97) group had significantly higher amplitude discrimination thresholds than those in the typically developing control group, those in the ASD (t(395) = 1.41, pTukey = 0.490; d = 0.34; BF10 = 0.50) and ADHD (t(395) = 2.24, pTukey = 0.113; d = 0.60; BF10 = 1.62) groups did not. The ASD + ADHD group was also less accurate than children in the typically developing control group (t(358) = 2.65, pTukey = 0.041; d = 0.70; BF10 = 3.57). The same pattern was observed when the amplitude discrimination protocols were completed with single and dual-site adaptation  = 4.40, p = 0.005; η2p = 0.03; BF10 = 63.40) and accuracy  = 3.57 p = 0.014; η2p = 0.03; BF10 = 20.85). There was also a non-significant trend towards a main effect of group on median reaction times  = 2.13, p = 0.096; η2p = 0.02; BF10 = 0.36). There was no significant main effect of group on the number of reversals  = 0.45, p = 0.721; η2p = 0.003; BF10 = 0.008). Compared to the typically developing control group, frequency discrimination thresholds were significantly worse in the ASD + ADHD (t(430) = 3.60, pTukey = 0.002; d = 0.70; BF10 = 58.45) group. The ASD (t(399) = 1.16, pTukey = 0.654; d = 0.34; BF10 = 0.38) and ADHD (t(399) = 1.49, pTukey = 0.444; d = 0.59; BF10 = 0.43) groups otherwise had similar frequency discrimination thresholds to those in the typically developing control group. Only the ASD + ADHD group were less accurate than the typically developing control group (t(399) = 3.27, pTukey = 0.006; d = 0.70; BF10 = 20.12). While not presented here, the same pattern of results was identified for group comparisons made on performance outcomes when the stimuli were delivered sequentially  = 2.55, p = 0.056, η2p = 0.03; BF10 = 1287.39) and accuracy  = 1.87, p = 0.135, η2p = 0.06; BF10 = 1,034,282.53). There were no group effects on median reaction times  = 1.42, p = 0.237, η2p = 0.01; BF10 = 0.01) or the number of reversals  = 1.61, p = 0.186, η2p = 0.01; BF10 = 0.07). Order judgement thresholds were significantly higher  in ASD + ADHD group compared to those in the typically developing control group (t(260) = 2.65, pTukey = 0.042; d = 0.48; BF10 = 4.48). Order judgement thresholds in the ASD (t(260) = 0.77, pTukey = 0.869; d = 0.25; BF10 = 0.33) and ADHD groups (t(260) = 1.51, p = 0.433; d = 0.24; BF10 = 0.47) were otherwise comparable to those in the typically developing control group. Compared to the typically developing control group, accuracy was significantly lower in the ASD + ADHD group (t(288) = 3.78, pTukey = 0.001; d = 0.60; BF10 = 124.36) and there was some evidence the ADHD group was also less accurate than controls (t(288) = 2.53, pTukey = 0.058; d = 0.21; BF10 = 2.66).We first note that a smaller sample  completed the order judgement protocol, as this was introduced into the protocol later into data collection , detection thresholds showed the strongest and most significant associations to items of the Sensory Processing Measure and Sensory Experience Questionnaire  and hypo-responsivity  and were reported to display more sensory seeking behaviors . Children with higher detection thresholds also had more reported problems with social participation  and body awareness . These associations provide support to the common suggestion that alterations in “lower-order” fundamental aspects of sensory processing could explain some of the more complex “higher-order” behavioral social problems experienced by those with ASD and ADHD43.Individuals with higher detection thresholds had more caregiver reports of problems with social and non-social combined scores of sensory hyper-responsivity , and frequency  discrimination thresholds had more reported problems with communication on the Autism Diagnostic Observation Scale  were administered the Autism Diagnostic Observation Scale and had parents/caretakers complete the Autism Diagnostic Interview-Revised, children with a primary diagnosis of ADHD are not included these analyses. After correcting for multiple comparisons, only two associations between the tactile sensitivity thresholds and items from the ASD-specific subscales remained significant see Fig. . Namely,r = 0.57, pBonferroni < 0.010) and DuPaul’s rating  scales remained  also remained . See Fig. For the correlations between the tactile sensitivity thresholds and the items from the ADHD-specific questionnaires, only children who had a primary diagnosis of ADHD were included . After correcting for multiple comparisons, significant associations between detection thresholds and hyperactivity subscales on both the Conners . Importantly, although detection thresholds were associated with items from the Sensory Processing Measure and Sensory Experience Questionnaire, detection thresholds were not meaningfully correlated to any of the items on the ASD-specific clinical scales . Following the pattern with the ADHD-specific alterations, amplitude and frequency discrimination thresholds, which had shown a pattern of being more commonly elevated in those with ASD, scaled exclusively with communication scores on the Autism Diagnostic Observation Scale, such that children who had higher discrimination thresholds were also those who had more reported problems with communication. This general pattern was also found for order judgement thresholds, such that elevated order judgment thresholds, more common in children with a diagnosis of ADHD, exclusively correlated with inattention scores on ADHD-specific clinical scales. Indeed, children with ADHD who had wider temporal binding windows  also had more reported difficulties with inattention.49 suggests that difficulties with processing in one sensory domain are usually accompanied by similar difficulties processing in another. That is, although different senses are primarily processed by distinct sensory-specific areas of the brain50 , certain aspects of sensory processing rely on shared brain structures and/or neuronal mechanisms52. In support of this, despite our finding of elevated tactile discrimination thresholds in ASD, atypical (either reduced or enhanced) discrimination of visual55 and auditory57 stimuli has also been identified in ASD. Similarly, the difficulties with order judgement that we identified in our ADHD and ASD + ADHD groups have also been identified when using visual and auditory stimuli58. In short, the disorder-specific alterations of tactile processing we identified likely reflect a dysfunction of more fundamental and generalized neurophysiological functions required for processing across sensory domains , rather than a dysfunction of sensory processing specific to the tactile domain . With that in mind, although the findings of the current study are restricted to tactile processing, it is possible (and perhaps likely based on prior evidence) that our findings would also hold across other sensory domains.Prior to discussing the neurophysiological implications of our findings, it is worth reiterating that although we have identified disorder-specific patterns of alterations in the tactile domain, prior evidence from clinical cohorts61. With specific regard to difficulties with amplitude and frequency discrimination in the current study, postmortem comparisons of the brains of adults with and without ASD have found that that the brains of adults with ASD had fewer lateral inhibitory connections between cortical minicolumns62. Given that lateral inhibition via GABAergic interneurons between cortical minicolumns is fundamental for appropriate discrimination of sensory input63, our finding of specific difficulties with tactile discrimination in children with ASD is consistent with the broad idea of atypical GABA inhibition in ASD. We have also previously identified an association between tactile discrimination thresholds and local GABA levels in S1 in children with ASD34.While many hypotheses have been proposed to explain the sensory alterations in ASD, the current prevailing hypothesis is that of abnormal GABA mediated neurotransmission64, GABA is also required for discrimination in other sensory modalities. For example, in the visual system, lateral inhibition is used to facilitate edge detection, which in turn improves the contrast and resolution of visual stimuli65. Similarly, given that tonotopic organization is maintained throughout the auditory system67, lateral inhibition of neighboring cells that respond to similar frequencies to those being actively processed is required for accurate tone discrimination. Beyond lateral inhibition, fast-spiking GABAA receptors (which are suggested to be affected in ASD68) also play a critical role in synchronizing spike timing of cortical neurons involved in encoding spatiotemporal characteristics of sensory stimuli69. Given the many roles that GABA plays in sensory perception, it should not be surprising that dysfunction of the GABA system could have wide ranging implications for overall neurodevelopment.It is important not to understate the importance of GABA for sensory perception as a whole. Just as GABAergic lateral inhibition is required for sharpening the receptive fields of tactile sensory input to improve discrimination70, social cognition is dependent on downstream input from sensory regions. If the sensory information is not appropriately processed (and hence compromised), then the capacity of the medial prefrontal cortex and superior temporal sulcus to make use of that information may be limited. Indeed, many important social cues require a degree of sensory discrimination. For example, emotions in speech are conveyed by slight changes in both the amplitude and frequency of tone71. One can imagine that difficulties with discriminating in the auditory domain could then result in failures to identify the correct emotion behind spoken sentences. Similarly, in the visual domain, difficulties with visual discrimination could also conceivably affect accurate identification of emotions communicated through small changes in facial expressions. Indeed, atypical tone73 and face76 discrimination have been previously identified in ASD. Especially from the perspective of developmental stages, altered sensory processing in critical periods of development  could have serious long-term consequences on overall social functioning.While speculative, it is possible that dysfunction of the GABA system, which in turn results in alterations of lower-order and domain-general alterations of sensory processing, contributes to many of the higher-order behavioral symptoms of ASD. Take for instance, the deficits in social communication and interaction that is characteristic of ASD. Although regions such as the medial prefrontal cortex and superior temporal sulcus are more typically referred to when discussing the “social brain”77 . It is difficult to speculate why GABA may associate with either differences in detection or discrimination, since GABA is involved in both. However, magnetic resonance spectroscopy of GABA is only one measure pertaining to the GABAergic mechanism whereas differences in detection or discrimination could be indicative of alterations in a number of processes related to GABAergic inhibition. While associations between elevated detection thresholds and S1 GABA have not yet been identified in ADHD, magnetic resonance spectroscopy studies have shown atypical GABA in multiple brain regions in those with ADHD80. Similarly, evidence from transcranial magnetic stimulation studies have also suggested atypical GABAergic receptor functioning of the cortex in children with ADHD82. In our own work, we have also previously identified an association between elevated detection thresholds and reduced S1 GABA levels in Tourette’s syndrome83, a disorder showing high comorbidity rates with ADHD84. As a whole, these data indicate the possibility that S1 GABA, in either its tonic availability or its phasic functioning at the receptor level, is involved in ADHD pathophysiology and may explain the elevated detection thresholds seen in the current study86.Elevated detection thresholds have been associated with lower levels of S1 GABA in ASD88. For example, even within the findings from the group comparisons made on the reaction time protocols from our battery (see Supplementary Results), children with ADHD and ASD + ADHD showed significantly longer and more variable reaction times than typically developing controls when required to make a speeded response to suprathreshold tactile stimuli. Dopamine is commonly implicated in timing functions90 and in the neurobiology of ADHD91. Treatment using dopaminergic psychostimulants in ADHD are also found to improve both order judgement thresholds and reduce reaction time variability90, suggesting that elevated order judgement thresholds arise out of dysfunction of the dopaminergic system in those with ADHD.With respect to elevated order judgement thresholds, timing deficits are a consistent finding in individuals with ADHD and appear to be present in their performance across a wide range of behavioral paradigms associated with the perception of time, but not specifically related to order judgementr > 0.75, pBonferroni < 0.001 for all three combination of correlations, see Supplementary Results, Supplementary Fig. The associations between static detection thresholds and both hyper-responsivity and hypo-responsivity are of strong interest. First, they broadly suggest that tactile sensitivity assessed using psychophysics correspond with caregiver reports of problems with sensory processing, showing concurrent validity. Second, detection thresholds being associated with both hypersensitivity and hyposensitivity is interesting in that this would appear to run counter to the common narratives of hyper-responsivity and hypo-responsivity as either distinct traits or opposite extremes along a single axis. In fact, we found that hyper-responsivity and hypo-responsivity and sensory seeking were all strongly and positively correlated . Moreover, while we assessed a range of protocols to assess different aspects of tactile sensitivity, the protocols adopted here are not exhaustive. For instance, while all of the tactile stimuli delivered in our battery were in the flutter range (0–50 Hz), it is unclear whether the pattern of results identified here would hold for higher frequency stimuli. Indeed, different cortical mechanisms may underlie even very similar tasks with slightly different parameters . Future investigations taking a more mechanistic approach to understanding how and why these alterations manifest at the neural level  have the potential to inform treatment development at an individual, rather than at a diagnostic level.To summarize, the results of this study point to disorder-specific alterations of tactile processing in ASD and ADHD, showing that tactile perceptual differences are uniquely related to the core symptoms of each disorder. Children with ADHD that had higher detection and order judgement thresholds also had greater problems with hyperactivity and inattention. Similarly, children with ASD that had higher discrimination thresholds also had greater problems with social communication. When taken together, these findings highlight the relevance of sensory, specifically tactile, processing alterations to the core symptoms of ASD and ADHD, supporting theories that altered sensory processing may cause or exacerbate core symptoms of these disorders. The relationships between these more fundamental aspects of tactile processing  and symptom expression in these disorders would suggest tactile processing alterations to be useful biomarkers of disorder-specific symptom severity. While sensory alterations have been described previously, here we show distinct and shared sensory alterations between ASD and ADHD that may inform future approaches in how we diagnose and treat these different disorders.94. A summary of the relevant descriptive variables, including age, sex, and handedness for each group that met screening/inclusion/exclusion criteria (described in detail below) are presented in Table A total of 434 children were included in this study, of which 197 were typically developing controls, 34 had ASD (but no diagnosis of ADHD), 99 had both ASD + ADHD, and 104 had ADHD (but no diagnosis of ASD). We note that while the frequency of comorbidity  in our sample may seem uncharacteristic of the true frequency of comorbidity in the population, a consensus estimate of the true frequency of comorbidity in the population has not been established. Studies have reported comorbidity rates between 40 and 76%95. Diagnostic status was confirmed or established using the criteria described below.Participants were recruited through local schools, with additional resources including community-wide advertisements, advocacy organizations, and medical institutions. Screening was performed via telephone interview with the parent or caregiver of each child. Children with a history of intellectual disability, seizures, brain injury, or other neurological disorder  or illnesses were excluded. Children who were taking psychotropic medications other than stimulants were excluded from participation. For children who were on stimulant medication, children temporarily ceased taking stimulant medication on the day prior to and the day of testing. Handedness was evaluated using the Edinburgh Handedness Inventory96 and/or the Kiddie Schedule for Affective Disorders and Schizophrenia97.All typically developing controls were free of criteria for psychiatric disorders of the Diagnostic Interview for Children and Adolescents Fourth Edition (DICA-IV)98 and/or Fifth Edition (DSM-V) criteria for ASD. This was confirmed with the Autism Diagnostic Observation Schedule-Generic99 or Autism Diagnostic Observation Schedule Second Edition45 and the Autism Diagnostic Interview-Revised44. The information was reviewed and verified by S. H. M., a child neurologist with over two decades of experience in diagnosing ASD in clinic and research settings. Any children in the ASD cohort with identifiable causes of autism  were excluded.Children in the ASD cohort met the Diagnostic and Statistical Manual of Mental Disorders Fourth (DSM-IV)100 or Fifth101 edition. Children with full-scale IQ scores below 80 were excluded from participation unless there was a 12-point or greater index discrepancy, in which case either the Verbal Comprehension Index or Perceptual Reasoning Index  was required to be over 80 and the lower of the two was required to be over 65. To rule out a learning disability in reading, children performed the basic reading subtest from the Wechsler Individual Achievement Test102 or the Wechsler Individual Achievement Test 2103. Those who demonstrated a significant discrepancy between full-scale IQ and the Wechsler Individual Achievement Test102 or Wechsler Individual Achievement Test 2 score or a basic reading subtest below 85 were excluded from the study. To rule out children who met criteria for psychiatric disorders other than ASD and ADHD, children were required to complete the Diagnostic Interview for Children and Adolescents Fourth Edition and/or Kiddie Schedule for Affective Disorders and Schizophrenia. If children met criteria for conduct disorder, mood disorders, generalized anxiety disorder, separation anxiety disorder, social phobia, or obsessive-compulsive disorder, they were excluded from the study.Intellectual ability was assessed with the Wechsler Intelligence Scale of Children-Fourth47 when available, or a score of 2 or 3 on at least 6 out of 9 items on the inattentive or hyperactivity/impulsivity scales of the ADHD-Rating Scale-IV; and 2) an ADHD diagnosis on the Diagnostic Interview for Children and Adolescents Fourth Edition and/or Kiddie Schedule for Affective Disorders and Schizophrenia. Further information was obtained through the Conners or Conners 3 Parent and Teacher Rating Scales-Revised: Long Form . The information was reviewed and then diagnosis was verified by S. H. M., a child neurologist with over two decades of experience in diagnosing ADHD in clinic and research settings. Children with a comorbid diagnosis of oppositional defiance disorder  were not excluded, since ODD does not represent a separate subtype of ADHD.Children in the ADHD group met the DSM-IV and/or DSM-V criteria for ADHD. Specifically, children in the ADHD group must have had: 1) a T score of 60 or higher on scale L (DSM-IV: inattentive) or M (DSM-IV: hyperactive-impulsive) on the Conners or Conners 3Children who met criteria for both ASD and ADHD were considered to have comorbid ASD and ADHD.104 (CM4). The CM4 stimulator has four 5-mm cylindrical probes capable of delivering vibrotactile stimuli in the form of sinusoidal pulses. All stimuli were delivered between 0–350 μm and between 0–50 Hz. In the case of the vibrotactile assessments used in the current study, participants placed all fingers of their left hand over each of the probes .Tactile perception was assessed via two-alternative forced choice paradigms delivered using a Cortical Metrics four-digit tactile stimulatorsee Fig. . For all106 that were completed by the participants, but are not reported in the main body of the manuscript are described in the Supplementary Methods. The results from the analysis of these data are also available as Supplementary Results. All the data and the code used to analyze these data are available on the OSF (https://osf.io/9mdc6/.).The full vibrotactile battery consisted of 11 protocols. For focus and clarity, in the main body of this manuscript we only report on static detection, amplitude and frequency discrimination, and temporal order judgment, as shown in Fig. To ensure participants had understood the protocol, each protocol condition was preceded by three consecutive practice trials that required correct responses to proceed. For all protocols, a stepwise adaptive tracking method was used (described in detail below). Participants could take as many breaks as possible except between protocols within the same domain.A suprathreshold stimulus  was pseudorandomly delivered to either left digit two or left digit 3. Participants were asked to respond on which finger they felt the stimulus. A one-up–one-down tracking paradigm (stimulus amplitude was decreased for a correct answer and increased for an incorrect answer) was used for the first ten trials and a two-up–one-down (two correct answers were necessary for a reduction in test amplitude) was used for the remainder of the task. There were 24 trials. Static detection threshold was calculated as the mean of the amplitudes of the last five trials.Stimuli were delivered to left digit 2 and left digit 3 simultaneously. One finger always received the standard stimulus (amplitude = 100 μm) while the other received the comparison stimulus . The two stimuli were delivered to either digit pseudorandomly. Participants were asked to choose which of two simultaneously delivered stimuli had the higher amplitude .A one-up–one-down tracking paradigm (comparison stimulus amplitude was decreased by 10 μm for a correct answer and increased by 10 μm for a wrong answer) was used for the first ten trials and a two-up–one-down was used for the remainder of the task . Amplitude discrimination thresholds were calculated as the mean of the amplitudes of the last five trials.Stimuli were delivered to left digit 2 and left digit 3 simultaneously . One finger always received the standard stimulus (frequency = 30 Hz) while the other received the comparison stimulus . The two stimuli were delivered to either digit pseudorandomly. Participants were asked which finger received the higher frequency stimulus.A one-up–one-down tracking paradigm (the comparison stimulus frequency was decreased for a correct answer and increased for a wrong answer) was used for the first ten trials. A two-up–one-down was used for the remainder of the trials. All conditions contained 20 trials and were delivered with an ITI of 5 s. Frequency discrimination thresholds were obtained as the mean of the frequencies of the last five trials.Each participant’s ability to judge the order of sequentially delivered tactile stimuli was tested. Two single-cycle vibrotactile pulses  were delivered to left digit 2 and left digit 3 separated temporally by a starting intertrial interval of 150 ms (the first pulse was assigned to either digit pseudorandomly) within a 1-s interval. A one-up–one-down tracking paradigm . Participants were asked to make a response based on which digit they believed received the first pulse. The protocol contained 20 trials delivered with an ITI of 5 s. Temporal order judgment thresholds were calculated as the mean of the intertrial interval of the last five trials.40 and Sensory Experience Questionnaire41 respectively. The Sensory Processing Measure is a norm-referenced assessment that produces scores for praxis, social participation, and sensory functioning across different sensory domains. The Sensory Experience Questionnaire was used in addition to the Sensory Processing Measure, since it was designed to evaluate the full range of sensory processing problems that are specific to ASD and provides an assessment of these problems in both social and nonsocial contexts. Both the Sensory Processing Measure and Sensory Experience Questionnaire have shown excellent reliability108 and validity109. The Sensory Processing Measure has also shown convergent validity with another popular measure of sensory processing, namely the Sensory Profile110.Sensory processing and experience was assessed by administering the Sensory Processing Measure111 (version 3.5.3). To determine whether the dependent variables of interest changed as a function of protocol condition within each group , the dependent variables of interest for each condition pair were first compared within groups using repeated-measures linear mixed-effects models using the lme4 R package112. In all models, age and condition were included as fixed factors and participant was included as a random factor. Age was included as a covariate due to our preliminary analyses finding evidence for group differences in age . Note that, while group differences were identified for IQ (as expected), IQ was not included as a covariate due to the reasons for not including IQ as a covariate in group comparisons for neurodevelopmental disorders described by Dennis and colleagues113. To test for significant effects, likelihood ratio tests were performed by comparing the log-likelihood statistic of the final models  to that of a reduced model . The alpha level was set at 0.05. For effect sizes, partial Eta-squared (η2p) for each model was estimated using the “effectsize”114 package in R. As a supplement to the null-hypothesis testing approach taken, we also ran Bayes factor analyses using the “BayesFactor”115 package in R. Unlike p-values, where a non-significant effect cannot be interpreted as support for the null-hypothesis, Bayes Factors (BF10) estimate the strength of evidence in favor of the alternative hypothesis .All statistical analyses were performed with Rd was estimated for each contrast. Where an interaction effect was present, simple slope analyses were conducted. The alpha level was set at 0.05 and p-values were adjusted for comparing a family of four estimates .Simple linear regression models were then generated to determine: (i) whether groups differed on the dependent variable of interest, as well as any other dependent variables that could help with interpretation of the results ; (ii) whether there was an effect of age on any of the dependent variables; and (iii) whether the effect of age on the dependent variables differed between groups . Thus, for these linear regression models, age, group, and their interaction were included as independent variables in the model. Where there was a significant main effect of group and an absence of a group interaction effect, two-tailed post hoc comparisons were performed using Tukey’s HSD test. To supplement interpretation of the post hoc comparisons, Cohen’s p-values to reduce Type 1 Error. All p-values were adjusted by multiplying the resulting p-value of each correlation between a given tactile sensitivity metric by the number of subscale items within each questionnaire. For example, for all of the correlations conducted between static detection thresholds and the items of the Sensory Processing Measure, the resulting p-value was multiplied by 7 .Following group comparisons, we conducted a series of correlation analyses at the sample-level to determine whether tactile sensitivity, as assessed via the vibrotactile battery, would correlate with questionnaire-based measures of sensory and related social functioning. Pearson’s correlation analyses were conducted between relevant tactile sensitivity metrics and scale items of the Sensory Processing Measure and Sensory Experience Questionnaire. Note that total scores (which are age-adjusted) were used for the Sensory Processing Measure, but raw scores (not age-adjusted) were used for the Sensory Experience Questionnaire. While the Sensory Processing Measure provides age-adjusted values, the Sensory Experience Questionnaire does not. Given the large number of correlation analyses being conducted here, the more conservative Bonferroni method was used to adjust the p-values for each analysis was applied to control for the increase in Type I error, for each subscale. If readers are interested, group comparisons made on the Sensory Processing Measure and Sensory Experience Questionnaire can be found in Supplementary Results, Supplementary Table Finally, we were interested in whether individual differences in tactile sensitivity for a were related to their disorder-specific clinical symptoms. Here, we conducted correlational analyses between the tactile sensitivity metrics to the domain or scales for each disorder-specific questionnaire . Again, Bonferroni adjustments of the Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary


The aim of this study was to develop and validate an algorithm to find patients with ADHD diagnoses within primary care electronic medical records (EMR); and then use the algorithm to describe the epidemiology of ADHD from 2008 to 2015 in a Canadian Primary care sample.Building and validating electronic algorithms to identify patients with specific disease profiles using health data is becoming increasingly important to disease surveillance and population health managementThis was a cross sectional time series that used data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a repository of primary care EMR data. A sample of electronic patient charts from one local clinic were manually reviewed to determine the positive predictive value (PPV) and negative predictive value (NPV) of an ADHD case-finding algorithm. In each study year a practice population was determined, and the algorithm was used to measure an observed prevalence of ADHD. The observed prevalence was adjusted for misclassification, as measured by the validity indices, to obtain an estimate of the true prevalence. Estimates were calculated by age group  and gender, and compared over time.The EMR algorithm had a PPV of 98.0%  and an NPV of 95.0% . After adjusting for misclassification, it was determined that the prevalence of patients with a clinical diagnosis of ADHD has risen in all age groups between 2008 and 2015, most notably in children and young adults . The well-established gender gap persisted in all age groups across time but was considerably smaller in older adults compared to children and young adults.Overall, the ADHD case-finding algorithm was found to be a valid tool to assess the epidemiology of ADHD in Canadian primary care practice. The increased prevalence of ADHD between 2008 and 2015 may reflect an improvement in the recognition and treatment of this disorder within primary care. Attention Deficit Hyperactivity Disorder (ADHD) is the most commonly diagnosed mental disorder in children and has a significant impact on an individual’s development, education and socialization throughout their life course , 2. LongADHD is one of the three most common disorders seen in primary care. As such, primary care providers are central to treating this condition . This caRecently, several studies have been published that evaluated the prevalence of ADHD using Canadian healthcare data. An Ontario study by Hauck et al. manually reviewed electronic medical records and applied predetermined criteria to identify ADHD cases . They foMeasuring and evaluating the primary care of children and adults with ADHD is important to promote their health and improve their long-term outlooks. As such, it is important to develop methods of detecting patients with an established ADHD diagnosis within the electronic medical record (EMR). The objectives of this study were to develop and validate an EMR case definition of ADHD diagnosis within a novel national dataset and report on the epidemiology of this disorder in children and adults within primary care.Ethics approval was granted by Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen’s University, Kingston.This study used Canadian primary health care data. Canada has a publically funded health care system where primary care providers are self-employed in private practices and act as gate-keepers to other specialists . While hThis study only included data from six of the 11 PBRNs that comprise CPCSSN due to limitations on data usage for industry funded projects. The data included in this study came from networks located in the provinces of Alberta, Manitoba, Ontario, Quebec and Newfoundland.In order to estimate the prevalence of patients who receive a clinical diagnosis of ADHD it is essential to determine the population at risk. This means identifying a practice population, individuals that are active patients and receive the majority of their primary care from the same clinic. This study used a two-year contact group to define the yearly practice populations. In each year of study (2008 to 2015), the practice population consisted of any patient with an encounter in the year of study or the preceding year. This method of using two years ofdata to determine a yearly practice population has been shown to most effectively capture the population at risk .Only patients with a valid entry for year of birth and gender were included.n = 246) and a random sample of patients (n = 246) not detected as having ADHD by the algorithm. The sample of subjects without ADHD were not age or sex matched to the subjects with ADHD as this algorithm was designed to capture ADHD diagnoses in all age groups (≥4 years of age) and both genders. While sensitivity and specificity could not be directly measured, as the validation sample was not a random selection of patients, we could get direct measures of the positive predictive value (PPV) and the negative predictive value (NPV) for our EMR algorithm. The sensitivity and specificity of the algorithm were derived using analytical expressions based on the Rogan-Gladen estimator [n = 492) that had a discordant ADHD classification between the algorithm and the chart abstractor was reviewed by the study epidemiologist to determine the source of misclassification.A patient was classified as having ADHD if they met the case definition for this disorder. A case definition was created and validated using a manual chart abstraction. The case definition, adapted from Kirby et al., used the medication, billing, diagnosis and encounter tables within the CPCSSN database to classify patients with ADHD . A patiestimator , 20. TheThe case definition was applied to each yearly practice population to produce a count of those with a clinical diagnosis of ADHD. This yearly count was used to determine the observed prevalence, defined as the proportion of the practice population that met the ADHD criteria. To obtain an estimate of the true prevalence in the database population the observed prevalence was adjusted for misclassification using the validity indices (NPV and PPV) determined by the validation of the chart algorithm. The analytical expressions used to calculate the true prevalence were recently published in a study by Bollaerts et al., and are available as a user-friend web application .Patients were classified into three age groups: 4–17 year olds, 18–34 year olds, and 35–64 year olds. These categories were chosen because the diagnosis and treatment of ADHD differs for school-age children and adults. In addition, young adulthood marks a distinct transition period in a person’s life course , 22. SenThe prevalence and corresponding confidence limits were computed using an exact binomial test. The analysis was performed in SAS statistical software (version 9.6®). The online application developed by Bollaerts et al. was used as the analytical tool to calculated adjusted sensitivity and specificity and estimate true prevalence values .Between 2008 and 2015 the practice population in each study year, of those at least 4 years of age, increased from 345,173 to 624,419 (Table n = 9) were due to management of the disorder by a specialist, and there was subsequently very little documentation  within the primary care EMR. The chart abstractor found record of the ADHD diagnosis within free text notes in the medical history or referral letters, which is data that isn’t captured within the CPCSSN database. The patients misclassified by the algorithm as having ADHD (n = 10) were almost all subjects where a provider had evaluated a patient for ADHD but a diagnosis or treatment plan did not follow. From these results, it was determined that the ADHD algorithm has a PPV of 95.9%  and an NPV of 96.3% . Using the analytic expressions developed by Bollaerts et al., the derived sensitivity is 19.9%  and the derived specificity is 100.0%  [The EMR algorithm, described in Table This case definition was used to measure the observed prevalence of patients with a clinical diagnosis of ADHD in each yearly practice population. Table As expected, there was a large gender gap in the prevalence of diagnosed ADHD in the younger age groups Fig. . In chilThis study developed and validated a method for identifying patients with established ADHD diagnoses using EMR primary care data in Canada. The algorithm has a PPV of 98.0% and an NPV of 95.0%. These measures of the validity of the algorithm allowed us to adjust the observed prevalence for misclassification and obtain estimates of the true prevalence of ADHD within the database population. Consequently, we can have confidence that applying the case definition to the EMR data captured within the CPCSSN database, and adjusting for misclassification, is an effective method to evaluate the prevalence of ADHD within primary care.This algorithm can be used to estimate the burden that this disease has on the population so that resources and interventions can be allocated where there is need. Furthermore, with expected developments in natural language processing, it is hoped that the free text within EMRs could be used to augment the algorithm. This means the algorithms would be more clinically relevant and could be used to screen a provider’s practice and find patients at risk for ADHD, or those that may have it and are not yet diagnosed.Between 2008 and 2015, the prevalence of patients with a clinical diagnosis of ADHD rose dramatically in children 4–17 years) and young adults (18–34 years). As expected, the prevalence of ADHD diagnoses is higher amongst children and young adults compared to older adults (35–64 years). Previous studies, including a systematic review and meta-analysis, have shown that the actual prevalence of ADHD is not increasing over time, which provides evidence that there is an increase in the identification and diagnosis of ADHD in primary care between 2008 and 2015, rather than an increase of this disorder in the population  years an, 21, 23.A recent study by Vasiliadis et al. used routine epidemiologic data from four provinces  to evaluate temporal trends and found a significant increase in the prevalence of diagnosed ADHD between 1999 and 2012 . While VThere was a small increase in the prevalence of ADHD diagnoses in adults 35 to 64 years old. This could be due to an increasing recognition that the symptoms of ADHD often persist into adulthood . HoweverThe distinct gender difference in the prevalence of ADHD found in this study is well-established in the literature –31. Howen = 19) revealed that most of the missed cases (n = 9) were a result of a diagnosis being recorded within free text notes, which is not captured within the CPCSSN database; and most of the falsely identified ADHD cases (n = 10) a result of evidence within the EMR (billing codes) of a provider examining a patient with symptoms congruent with ADHD, but not diagnosed with the disorder. While there is no reason to believe this misclassification differs across gender or time the algorithm was designed to capture ADHD in patients of any gender or age (≥4 years) and it is possible the validation measures of the algorithm could differ in specific groups. Lastly, this study used data from a convenience sample of primary care clinics from five provinces. As such, the yearly study populations may not be representative of all Canadians.There are a number of limitations to this study. Firstly, the case definition for ADHD was validated using a manual chart review from one clinic using one EMR product. While this provides confidence in the robustness of the algorithm it would be ideal to validate this case definition across several EMR products and within multiple clinics. It is possible that the results of the validation may only be generalizable to this clinic using this EMR product. Furthermore, an analysis of the subjects with discordant classifications between the algorithm and chart reviewer (The findings from this study demonstrate the reliability of an ADHD case finding algorithm in a large Canadian primary care database. Applying this algorithm it was determined that the prevalence of patients with a clinical diagnosis of ADHD increased between 2008 and 2015, likely as a result of increased recognition and treatment of this disorder within primary care. There still exists a significant gender gap. Many patients with ADHD rely on their primary care providers for the care and support they need to manage their disorder and its adverse effects. Establishing robust methods to measure and evaluate the care of patients with ADHD is important in order to ensure clinicians are providing the best care and support for their patients.Additional file 1.