Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two frequently co-occurring neurodevelopmental conditions that share certain symptomatology, including social difficulties. This presents practitioners with challenging  diagnostic considerations, particularly in clinically more complex cases with co-occurring ASD and ADHD. Therefore, the primary aim of the current study was to apply a data-driven machine learning approach (support vector machine) to determine whether and which items from the best-practice clinical instruments for diagnosing ASD  would best differentiate between four groups of individuals referred to specialized ASD clinics . We found that a subset of five features from both ADOS  and ADI-R  reliably differentiated between ASD groups (ASD & ASD + ADHD) and non-ASD groups (ADHD & ND), and these features corresponded to the social-communication but also restrictive and repetitive behavior domains. In conclusion, the results of the current study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms  while preserving relatively high diagnostic accuracy. Clinical implications and study limitations are discussed. Individuals with ASD show restricted and repetitive behaviors, persistent impairments in their social communication and interaction abilities and may also report sensory issues2. Typical ADHD symptoms entail impulsivity, hyperactivity, and inattention that cause serious functional impairments across different social settings3. While there is evidence to support the view that ASD and ADHD are separate mental health conditions, they appear to be closely related4. In fact, research suggests that diagnoses of ASD and ADHD frequently co-occur6. This points to considerable overlap at both the clinical and etiopathophysiological level, presenting for instance practitioners with challenging differential diagnostic considerations7. In particular, the correct classification of an individual’s clinical symptoms as ASD- and/or ADHD-specific is pivotal for a precise diagnosis, and this in turn is important for patients and their families in terms of adequate healthcare and rights for compensation and social support , which differ depending on the respective diagnosis/diagnoses8. Consequently, investigating the phenotypic boundaries and commonalities between both conditions has become a timely and urgent topic9.Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two of the most commonly diagnosed neurodevelopmental conditions in childhood and adolescence12. Similarly, core symptoms of ADHD, like inattention and hyperactivity, are often found also in children with ASD13. However, the extent to which the two conditions clearly share difficulties remains poorly understood. This is partly owed to the fact that the vast majority of relevant work is based on caregiver questionnaires or on broadband psychopathology screening measures that are rather unspecific diagnostically. More recently, though, different research groups have started to use best-practice diagnostic instruments for ASD to explore more thoroughly ASD-like impairments in ADHD, and to tests the validity of these measures to differentiate clinically between ASD and ADHD .To date, research has shown that children with ADHD often have social difficulties that may resemble those seen in ASD, including problems in relating to other people or inappropriate peer-related behaviors16, and the second is the Autism Diagnostic Interview-Revised (ADI-R) which is a parental interview17. With respect to the ADOS, good sensitivity and specificity has been demonstrated in research settings when analyzing individuals with “pure” ASD vs. those without any diagnosis (ND), while relatively little is known about its utility and accuracy in youth and adults with co-occurring psychiatric disorders18. Recently, Colombi et al. analyzed data from a clinical sample with a suspected ASD diagnosis that was assessed with the ADOS-2 modules 3 or 4 . Individuals of this sample where initially diagnosed with one of the following diagnoses: psychotic disorder, mood disorder, or disruptive disorder  but not ASD. Individuals were admitted consecutively to the psychiatric inpatient unit for acute psychiatric symptoms suspected of having ASD. Patients with a previous diagnosis of ASD were excluded. The study found generally low sensitivity and specificity in detecting/excluding ASD for both M3  and M4 (sensitivity: 55.6% and specificity: 59.5%). Therefore, the authors recommended caution when interpreting the results of the ADOS-2 in psychiatric individuals with a consisting (comorbid) diagnosis and a following ASD suspicion. However, they pointed out that their findings are limited because the study was conducted at a single site and with a limited number of participants. They recommended that the results ideally be replicated and extended with a larger sample from different sites using the two best-practice diagnostic tools for ASD, ADOS/2 and ADI-R19.There exist two widely used and scientifically established instruments developed specifically to evaluate ASD symptoms. One is the Autism Diagnostic Observation Schedule (ADOS) which is a direct clinical observation assessment20 of the accuracy of the ADOS-2 and the ADI-R in clinical compared to research settings found that the ADOS-2 is more accurate than the ADI-R in detecting ASD, and it was confirmed that sensitivity and specificity are less accurate in (retrospective) clinical analysis than in “real time” research settings, with specific in- and exclusion criteria. Therefore, the authors concluded that more research in clinical populations, in particular, is urgently needed. Using both ADOS  and ADI-R, Grzadzinski et al.14 investigated a clinically referred group of children who received best-estimate clinical (BEC) diagnoses. They focused on the presence of either ASD or ADHD. First, they compared the proportion of children in both groups who met standard ADOS and ADI-R cut-offs for an ASD diagnosis. The ADOS cut-off was met by 85% of the children with a BEC of ASD and by 21% of the children with a BEC of ADHD, while the ADI-R cut-offs were met by 67% of the children with a BEC of ASD and by 30% of the children with a BEC of ADHD. The authors then focused on ASD symptoms that most adequately differentiated between the two conditions by only including symptoms which were endorsed in ≥ 66% of the group with a BEC of ASD and ≤ 33% of the group with a BEC of ADHD (note that “restricted and repetitive behavior” items were not considered because endorsement was expected to be low). There were four ADOS items from the social communication domain , but none of the ADI-R items, that met this criterion for adequate discrimination.Interestingly, a recent review and meta-analysis21, children with ADHD however display a large amount of ASD-like symptoms—certainly those individuals who are clinically referred due to ASD concerns, underscoring the fact that social difficulties are also common in many individuals with ADHD22.The results of the Grzadzinski study clearly highlight the challenge of practitioners to distinguish a child with ASD from one with ADHD in clinical settings even using best-practice instruments. While differences in the type and quality of social impairments between ASD and ADHD seem to exist25), it is critically and clinically relevant to know whether and which specific symptom sets best characterize and separate not only both pure groups, but all three groups. A better understanding of such phenotypic differences and similarities could inform clinical interventions that are more targeted27.Notably, the above-mentioned study did not include children with a co-occurring ASD + ADHD. Thus, it remains unclear to what extent the two best-practice diagnostic instruments in the field of ASD are actually able to reliably distinguishing between individuals with ASD or ADHD versus those with the co-occurrence of both conditions. Considering that ASD + ADHD may qualify as a potentially distinct subtype compared to the two single diagnoses .Recently, clinical research has begun to use machine learning (ML) methods to identify useful phenotypic information from the ADOS39 were the first to apply ML algorithms to distinguish ASD from ADHD by using the 65-item Social Responsiveness Scale (SRS), which is a parent-reported screening questionnaire on the severity of autistic characteristics40. This study found that five behaviors measured by the SRS  were sufficient to distinguish ASD from ADHD with high accuracy . However, Duda et al.39 did not apply ML on either ADOS or ADI-R, and—like Grzadzinski et al.14—they did not include individuals with co-occurring ASD + ADHD or those without any clinical diagnosis.To our knowledge, Duda et al.41, see also “42, SVM is the most accurate and widely used classifier in distinguishing individuals with ASD from those without ASD, ensuring the likelihood that results can be replicated (see Ref.43) for a discussion of this issue in the context of ML). Since most relevant ML work has been conducted in English-speaking countries (primarily the US), we aimed to extend this line of research with a large and diverse multi-site German sample to increase generalizability of the analyses and findings, and thus improve our international understanding of the differential diagnosis of ASD vs. ADHD using ML techniques. Moreover, we focused on a dataset that included many individuals with the BEC diagnosis of ASD + ADHD to challenge the performance of the ML algorithm in light of the particularly difficult nature to differentiate patients with ASD from those with co-occurring ADHD by using the best-practice diagnostic instruments for autism. Our main a priori hypothesis was to find specific behaviors particularly in the social domain, such as difficulties in social communication, that best differentiate between ASD (incl. ASD + ADHD) and ADHD, but also between ASD (incl. ASD + ADHD) and ND. Being able to identify behavioral features that distinguish between the groups may help developing novel training and screening tools for differential diagnostics based on the identified set of features.Thus, the primary aim of the current study was to use ML  in order to determine whether and which ADOS and ADI-R items would be most informative to separate between individuals with BEC of ASD, ADHD and those with ASD + ADHD versus individuals with no psychiatric diagnosis (ND) who were all referred to specialized clinics due to ASD concerns. We chose SVM because SVMs have several advantages over other ML algorithms , and 11% of the individuals with ADHD (as compared to 21%) met ADOS/2 cut-offs for ASD. Additionally, and novel, 68% of the ASD + ADHD group and 15% of the ND group met the clinical cut-off on the ADOS. With respect to the ADI-R, 45% of the ASD group and 44% of the ASD + ADHD group in contrast to 17% of the individuals with ADHD and 8% of the individuals with ND met all three domain cut-offs. Finally, while combining both ADOS and ADI-R resulted in excellent specificity rates for individuals with ND and those with ADHD (between 97 and 100%), sensitivity rates ranged low between 30 and 34% for both ASD groups. Thus, using a combination of ADOS and ADI-R diagnostic cut-off scores decreased substantially the risk of false positives. However, it increased dramatically the risk of false negatives, for instance, as compared to the single usage of the ADOS. This has implications for clinical practice, including specialized ASD clinics that deal with a great number of referrals due to ASD concerns who eventually end up not having ASD but another psychiatric condition requiring proper assessment and treatment, or who have autistic characteristics without any psychiatric diagnosis. Our finding that ~ 65% of individuals with a BEC diagnosis of ASD did not reach the clinical cut-offs on either ADOS and ADI-R emphasizes that (i) ASD cannot simply be diagnosed by schematically applying cut-off scores alone, but (ii) additional clinical information from a patient’s medical record  need to be gathered to confirm the diagnosis in conjunction with professional clinical judgement.The primary aim of the current study was to determine whether and which ADOS and ADI-R items would be most informative to separate between the four groups of individuals referred to specialized ASD outpatient clinics . We first compared the percentage of individuals in each group who met clinical cut-offs for ASD on the ADOS and/or ADI-R, which was intended to replicate and extend the findings by Grzadzinski et al.The main innovative aspect of the current study was that we applied an independent and data driven machine learning approach using SVM to determine whether and which ADOS and ADI-R items would best differentiate within the clinical sample at hand. Here we focused on the five most important discriminative features . We found that a reduced subset of five features from both ADOS and ADI-R reliably differentiated between non-ASD and ASD groups, ranging from acceptable (ASD vs ND) to excellent  discrimination. Overall, our findings support earlier ML studies showing a limited number of crucial features contribute to classificatory precision that is just as accurate as leveraging diagnostic information from the full examination of ADOS and/or ADI-R. Notably, though, when comparing ASD with ND, the five-feature model performed worse than the all-feature model in separating both groups, suggesting a disadvantage of the former over the latter regarding its classification accuracy (AUC: 0.77 vs. 0.91). This is in contrast to the findings by Kamp-Becker and colleagues (2021) who used the random forest algorithm on ADOS and ADI-R combined and reported similar classification rates for full-feature and reduced-feature models when comparing ASD with a non-ASD group. However, their sample differed from ours as it included a large number of individuals with a wide range of different co-occurring psychopathologies, while the current sample had no other psychiatric diagnoses in addition to the primary BEC (ASD and/or ADHD) (or the lack thereof) assigned to the four groups tested here. This likely explains the different findings.It should be emphasized, though, that our five-feature classifier was still sufficient enough to reliably distinguish ASD from ND with 88% sensitivity and 81% specificity, which is higher than classificatory performance via the combination of conventional ADOS and ADI-R cut-off scores. Thus, it appears conceivable that the clinical assessment, including the screening for ASD, could be made a less complex endeavor by drawing clinicians’ attention to the most specific behavioral features of ASD in order to help in the  diagnostic process. In the long term, this can facilitate decision-making with regard to whether a patient with signs of ASD actually needs a comprehensive—usually multi-hour—standard clinical evaluation for ASD or not.The five most relevant items selected by the SVM algorithm to differentiate ASD (and ASD + ADHD) from ND, partly confirm our predictions. We indeed found that the majority of discriminative features belonged to the social domain, but several restricted and repetitive behavioral features were picked up by the algorithm, too, and stemmed from both ADOS and ADI-R. Notably, all identified features in the current study are also part of the clinical scoring algorithms as proposed by the ADOS/2 and ADI-R manuals which underscores their diagnostic utility. Two of these behavioral features were ranked among the most relevant ones for both ASD groups compared to ND, including unusual eye contact, and hand/finger mannerisms. If replicated in independent samples, this finding suggests that – irrespective of co-occurring ADHD—such behaviors  screening items had an excellent accuracy for distinguishing between the two conditions. In fact, similar to the Duda study, we also found that predominantly social  but also restrictive and repetitive behaviors  were among the most valuable classifiers in this regard. This pertained to both group comparisons—ASD vs. ADHD and ASD + ADHD vs. ADHD, while the latter one is a novel addition to the existing literature. Interestingly, “trouble with the flow of normal conversation” was the highest-ranking feature in the work by Duda et al.39. Although “conversation” (ADOS item A8) was not the highest-ranking feature in the current study when comparing ASD or ASD + ADHD with ADHD, it was the one that overlapped across both group comparisons, indicating its promise for evaluating the risk of ASD—irrespective of co-occurring ADHD—relative to pure ADHD. Notably, “conversation” was also the ADOS item with the highest importance value to accurately classify ASD vs. mood and anxiety disorder (MAD) in a recent study by Ref.45. In fact, five out of the eight items  listed in the Wittkopf study as the ones with the greatest classification significance were also selected by the SVM algorithm used here in the ASD versus ADHD group comparisons: conversation, facial expressions directed to others, stereotyped/ idiosyncratic use of words or phrases, amount of reciprocal social communication, and quality of social overtures. Note, these are all exclusively ADOS items, similar to the results reported by Grzadzinski et al.14. Taken together, these cross-study findings suggest that the presence of a minimal subset of observable behaviors, particularly in the social interaction and communication domains, appear to be ASD-specific in distinction to other often co-occurring mental health conditions, such as ADHD or MAD.Using SVM to compare ASD with ADHD, we found excellent classification accuracies for both the full-feature  and the five-feature model . These results corroborate and extend the ML findings by Duda et al.14 did not analyze ADOS and ADI-R items tapping into restricted and repetitive behavior when comparing ASD with ADHD because the authors expected endorsement to be negligible. Our findings  do not support this assumption. Although, as expected, the vast majority of features that best classified ASD relative to ADHD stemmed from the social interaction and communication domains (as outlined above), our ML algorithm also identified two items from the restricted and repetitive behavior domain, including stereotyped/ idiosyncratic use of words or phrases, and hand/finger mannerisms. These data may suggest that this behavioral domain is in fact less relevant for differentiating ASD from ADHD. At the same time, it may also signify that restricted and repetitive behavior items are generally less reliable than social-communication ones when coding them . One reason for such reduced objectivity might be the lower occurrence frequency of these behaviors which hampers their valid identification . Therefore, one could conclude to date that they are of limited usability in discriminating ASD from ADHD, or other mental health conditions such as MAD45.Remarkably, Grzadzinski et al.14 and Grzadzinski et al.39, we did not find “unusual eye contact” to be among the five highest ranking classifiers that differentiated ASD and ADHD. But we found that this particular behavioral feature separated well ASD (both groups) from the ND group (see above). This suggests that—in addition to several other ASD-related symptoms—atypical eye contact might be a behavioral feature that overlaps considerably in ASD and ADHD, at least in this clinically-referred sample . Therefore, the presence of abnormal eye contact appears to be rather ASD-unspecific as it does not adequately discriminate ASD from ADHD (or ASD from MAD45).In contrast to the studies by Duda et al.Unsurprisingly, the SVM algorithm performed poor in discriminating between ND vs. ADHD as well as between ASD vs. ASD + ADHD (AUCs ≤ 0.62). Interestingly, with regard to ND vs. ADHD, the algorithm picked several discriminative features that are apparently ADHD-related, such as overactivity, or disruptive behaviors. However, considering the inadequate classification accuracy of the five-feature model that identified these features, this finding lacks reliability and thus should not be further interpreted.21; see also49 who found no phenotypic differences between ASD and ASD + ADHD when measured with ADOS and ADI-R).Concerning the ASD vs. ASD + ADHD comparison, our data would not support the notion that the group of individuals with ASD + ADHD qualify as a potentially distinct subgroup than ASD without ADHD. Both groups were diagnostically quite indistinguishable when the current best-practice assessment instruments, ADOS and ADI-R, are administered and analyzed with machine learning. This corroborates our prior findings using ‘traditional’ variance analyses on the ADOS/ADI-R in form of a full-factorial group design  are more capable of detecting specific features for differential diagnostic purposes. Second, we analyzed the diagnostic data from children, adolescents, and adults combined. While certain ASD-related behaviors appear to be stable over the life course, there is also evidence to suggest that ASD symptom presentation may change from child- into adulthood with general improvements in symptomatology with age50. However, we intentionally decided to not split and compare the four groups by specific age bands due to limited statistical power. Note that we already used the SVM algorithm on six group comparisons. Thus, splitting the sample into for instance two age bands, children/adolescents versus adults, would have inflated our analyses to 12 comparisons rendering a proper interpretation nearly impossible. Moreover, we were only able to include relatively few older individuals with ADHD. Hence, dividing the sample by age would have led to unmanageably small subgroups to compare. However, it would be interesting to see how our machine learning approach performs in adequately powered groups of youth versus adults.The findings of the current study need to be interpreted in light of a few limitations. First, the entire sample, including the individuals without a psychiatric diagnosis (ND group) as well as those with ADHD without ASD, was recruited via specialized ASD clinics in Germany. This may have biased our results to some extent because both groups, ND and ADHD, were pre-selected due to ASD concerns and thus are not fully representative subsamples of their respective population. This selection bias may also have resulted in the two ADHD groups being slightly younger and having a higher male-to-female ratio than the two other groups . Nevertheless, the sample as a whole is unique as it stems from a clinical rather than a traditional research population, representing a ‘typical’ population in German outpatient clinics for ASD which is most relevant to practitioners . Note, though, that we deliberately excluded nonverbal individuals and those with other co-occurring psychiatric diagnoses, thereby limiting the generalizability of our findings to the entire ASD population. To advance this line of research forward, it would be ideal to recruit and analyze clinical (and/or research) samples across different countries that are not limited to specialized ASD clinics, but also include other data sources to replicate the results obtained and thus improve the interpretation and generalization of the current findings. This could also entail a comparison of different ML algorithms, as done by Ref.Finally, we restricted our SVM analyses to select only the five most discriminable features per group comparison. Although one can argue that this number is rather arbitrary, it corresponds to the smallest number of ADOS/ADI-R features previously identified by different machine learning algorithms when comparing ASD with non-ASD groups. Therefore, the present approach may facilitate the comparability of findings reported in other relevant work current and in the future.38 for a critical view on current trends in the assessment of ASD, including the shortening of the diagnostic process). We certainly acknowledge that further studies in independent samples are warranted to further determine the clinical utility of the identified diagnostic classifiers. If replicated, these results may benefit the development of novel practitioner-oriented training tools to detect ASD (vs. other conditions) more efficiently and/or optimize the screening, triaging and diagnosing of those individuals seen in specialized institutions by aiding clinical experts particularly in the challenging process of differential diagnosis. Notably, individuals who are referred to ASD outpatient clinics are usually not evaluated for ADHD as part of the assessment routine due to numerous pragmatic considerations . Thus, one could contemplate the feasibility of developing some kind of ADHD screening score based the behavioral classifiers from ADOS/ADI-R that best separated ADHD from the other groups. This could guide the decision-making process of whether an additional evaluation for ADHD is clinically justified or not.In conclusion, the results of the present study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those clinically more complex cases with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms  while preserving relatively high diagnostic accuracy  of whom 1260 (51.4%) were diagnosed with ASD, others had another mental condition , and 349 (14.2%) did not receive any psychiatric diagnosis. All individuals were diagnosed according to the International Classification of Diseases ICD-1052 using “gold standard” best estimate clinical (BEC) diagnoses53. Any BEC diagnosis was determined by at least two experienced clinicians from a multidisciplinary team (incl. psychologists and/or psychiatrists) after extensive examination and review of all available information from a patient’s medical record that included—amongst others—IQ test results, the Autism Diagnostic Observation Schedule . Due to the retrospective nature of our data collection and analysis, the need for informed consent was waived by the ethics committee. All methods were performed in accordance with the relevant guidelines and regulations.The sample was derived from Germany’s largest database of individuals referred to specialized ASD outpatient clinics55. This resulted in a final sample of n = 1195 individuals , including n = 574 individuals with ASD, n = 164 with ADHD, n = 113 with ASD + ADHD, and n = 344 with ND  referred for a clinical ASD diagnostic assessment, (ii) complete data of ADOS Module 3 or Module 4, and ADI-R, (iii) verbally fluent, (iv) BEC diagnosis of ASD  but no ADHD, (v) BEC diagnosis of ADHD  but no ASD, (vi) co-occurring ASD + ADHD, and (vii) no psychiatric diagnosis (ND). The patients with ASD, ADHD and ASD + ADHD had no other psychiatric diagnoses. With respect to the ADOS, we focused on modules M3 and M4 indicating verbal fluency, because differentiation between ASD and ADHD is especially challenging in verbally fluent patients with average intellectual functioning57: ADOS58 and ADI-R60. Both are based on ASD criteria of ICD-1052 and DSM-IV-TR61, and they can be used to obtain information about ASD symptoms across different behavioral domains. More details on these two well-established measures can be found in the Supplement.The current analyses included the item scores of the so-called “gold standard” or best-estimate instruments in diagnosing ASD14. We applied the following cut-offs for the ADOS/2 . According to the ADI-R manual, no total cut-off is to be calculated. However, for the current analysis, we defined the criteria, like Grzadzinski et al.14, that patients who met ASD cut-offs on the three ADI-R domains  reached the ADI-R total cut-off.First, we calculated the proportion of individuals in each of the four groups who met diagnostic algorithm cut-offs in both ADOS and ADI-R, following the analytic procedure analogous to Ref.41. SVM is used to find a hyperplane with the maximum margin  in an n-dimensional space (where n is the number of features) to differentiate between classes62. For instance, SVM has been shown to perform with high accuracy particularly in distinguishing between ASD and ADHD by utilizing only 5 screening items from the SRS39. SVM is the one algorithm that is most frequently used in this line of research likely due to its high predictive power for ASD classification37. According to a recent meta-analysis42, SVM is the most accurate classifier in distinguishing individuals with ASD from those without ASD, ensuring the likelihood that results can be replicated (see43 for a discussion of this issue in the context of the use of ML for ASD diagnostics). SVMs retain several attractive properties: They can deal with noisy, highly correlated features and high-dimensional data sets, and they are resistant to overfitting and thus generalize well41.We then used support vector machine (SVM) analyses to evaluate which ADOS and ADI-R items are able to discriminate best between ASD (incl. ASD + ADHD) and ADHD, but also between ASD (incl. ASD + ADHD) and ND. SVM is a robust machine learning algorithm, which can be used to examine data for various purposes, such as classification or regression analyses, in order to solve big data classification problems39. We then tested whether the two approaches differed significantly from each other to validate the five-feature model versus the all-feature model.We adopted a binary classification approach, i.e., we examined pairs of diagnostic groups, resulting in six possible combinations  can be found in the Supplement.Within SVM, we used the k-fold method for cross-validation (CV) with k = 10, which minimizes the risk of false positive features. This CV method randomly divides the data into k portions in which k − 1 portion is considered as training data and the other portion as testing data. By continuing this k-times, all subjects in the data set are part of both the training and testing set. The resulting classification accuracy is the average of all k-folds


Attention‐deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered.In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed‐based whole‐brain resting‐state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta‐analysis was performed to investigate the abnormal connectivity in different age groups . Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset.As with previous studies, scarce results were found in the all‐age group. However, we found that the younger group consistently exhibited hyper‐connectivity between different parts of the cortex and left middle frontal gyrus, and hypo‐connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper‐connectivity between the cortex and right precuneus/sub‐gyral/cingulate gyrus. Besides, the abnormal cortico‐cortical and cortico‐subcortical functional connectivity in children, and the abnormal cortico‐cortical functional connectivity in adults were verified in our independent dataset.Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico‐cortical and cortico‐subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico‐cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. Cortico‐cortical and cortical‐subcortical connectivity might be an essential aspect in the pathophysiology of children with ADHD, while abnormal cortico‐cortical connections were more important for older ADHD (mainly for adults with ADHD). During human growth and development, the brain undergoes a series of complex and coordinated processes such as neural differentiation, synaptogenesis, myelination, and synaptic pruning.Resting‐state functional magnetic resonance imaging (rs‐fMRI) is a non‐invasively effective tool for assessing the development of the human brain.Though large sample size longitudinal structural brain abnormalities across the development of ADHD have been found in many studies, and former studies have demonstrated the significant association between brain structure and brain function,Though there are already three meta‐analyses about resting‐state functional connectivity (rs‐FC) of ADHD, these results were not always consistent with each other. Two of them focused on predefined networks and found abnormal inter‐network and intra‐network connectivity in ADHD.1.2Various brain networks and large‐scale intrinsic neural circuits including multiple cortico‐cortical and cortico‐subcortical regions have been found to be involved in ADHD.Recently, Casey et al. proposed hierarchical changes in the circuitry of the emotional brain: from subcortico‐subcortical in children, then to subcortico‐cortical/cortico‐subcortical in adolescence, and finally to cortico‐cortical in young adulthood.Therefore, in this study, we aimed to make a meta‐analysis to explore the convergence of rs‐FC changes for ADHD from the perspective of development. Given the different development patterns of cortex and subcortex, here we divided the studies into two groups (cortex and subcortex) according to the localization of seeds. The main questions here are (1) whether could we find more spatially converge findings across studies after dividing the studies according to sample age and (2) how abnormal connections of subcortical and cortical circuits change with age. Finally, we also performed a validation experiment in our own dataset to verify the results in the meta‐analysis.22.12.1.1Our literature search was conducted in PubMed, Scopus, Web of Science, and Embase databases on October 12, 2020 (including studies in‐press). Details of the search strategy were reported in the supplementary materials  showed the largest case‐control differences in surface area and cortical thickness, respectively.See Supplementary Appendix 2.1.2GingerALE v3.0.2 was used to investigate brain connectivity differences between ADHD and healthy control (HC). Activation likelihood estimation (ALE) is a widely used technique for coordinate‐based meta‐analyses. See Supplementary Appendix p < 0.001; cluster‐level: p < 0.05) was set.Two principal analyses were conducted. Firstly, to detect spatial convergence of cortex and subcortex in patients with ADHD, we performed analyses in the following 4 groups: “Cortex_ADHD > HC ,” “Cortex_ADHD < HC,” “Subcortex_ADHD > HC,” and “Subcortex_ADHD < HC.” Secondly, we divided these four groups into “younger” and “older” based on their mean age, respectively, and repeated the above analyses. As recommended by Veronika I. Müller, cluster‐level FWE  correction was performed to correct for multiple statistical comparisons, voxel 33.1Figure 3.1.1The anatomical location of foci reported in prior articles is shown in Supplementary material Figures . First, The patients were further grouped based on mean age (<12 years and ≥12 years). The subsequent analyses were conducted only for “Cortex_younger_ADHD > HC,” “Cortex_older_ADHD > HC,” “Subcortex_younger_ADHD > HC,” “Cortex_younger_ADHD < HC,” and “Subcortex_younger_ADHD < HC,” as others could not provide at least 8 studies per group. Detailed information of ALE analyses could be found in the Supplementary materials Table .For the younger group, we found that ADHD group showed hyper‐connectivity between different seeds in the cortex and left middle frontal gyrus (MFG) Figure , and hypExamining contributions of significant results indicated that for “Cortex_younger_ADHD > HC,” the results were mainly driven by studies with seeds in frontal cortex. For “Cortex_younger_ADHD < HC,” the abnormal connection was mainly driven by studies with seeds in temporoparietal cortex and frontal cortex. While for “Cortex_older_ADHD > HC,” the abnormal connectivity was driven by studies with seeds in frontoparietal lobe Table . Besides3.2For children, the FC from the left MFG as seed to the right anterior cingulate, which is in frontal cortex areas, was increased in the ADHD group when compared with that in the healthy matched individuals Figure . ADHD alFor adults, patients with ADHD showed significant increased FC between right precuneus/sub‐gyral/cingulate gyrus and right precentral gyrus, left precentral gyrus, and right supramarginal gyrus, in the frontoparietal cortex Figure , Table 3That is to say, the results in the independent analysis not only validated the abnormal FC of cortex‐cortex and cortex‐subcortex in the meta‐analysis but also validated the corresponding brain areas in the meta‐analysis.4With ALE, we investigated the spatial convergence of ADHD‐related hyper‐ and/or hypo‐connectivities in the cortex and subcortex, across development (<12 and ≥12 years). In the all‐age group, we only found aberrant activity in the “Cortex_ADHD < HC” group. However, further analysis showed that for the younger group, the seeds in the cortex consistently showed hyper‐connectivity with middle frontal gyrus, whereas hypo‐connectivity with left putamen/pallidus/amygdala. While for the older group, more precisely, for adults, the cortex seeds showed a consistent hyper‐connectivity with right precuneus/sub‐gyral/cingulate gyrus. Moreover, the results of the independent analyses not only validated the abnormal cortex‐cortex and cortex‐subcortex FCs patterns observed in the meta‐analysis but also showed overlapped brain regions with that identified in the meta‐analyses.As in the introduction, there are already three rs‐FC meta‐analyses about ADHD, which consistently suggested a potential developmental effect on FC in ADHD. However, no further effort was performed to elucidate the precise developmental influence in these studies. Compared with these studies, our current study enrolled more rs‐FC original studies Tables  and mainOur study showed that both the younger and the older patients with ADHD were with hyper‐connectivity among cortical regions, while the younger group also indicated additional hypo‐connectivity between cortex and subcortex. This might be explained by the existence of developmental delay in ADHD. Multiple analyses of FC showed that the development of large‐scale brain networks is presented with weakening of short‐range FC and strengthening of long‐range FC.There was a substantial amount of structural‐developmental studies of brain with a large sample size in the last few decades. For cortex, Shaw et al. conducted a series of longitudinal brain imaging development research between ADHD and HC. They found that ADHD and HC have similar developmental trajectories, but the median age by which 50% of the cortical points attained peak thickness for the ADHD was 3 years later than HC,In this study, spatial convergence in cortico‐subcortical connectivity was only found in the younger group and the older group even did not have enough data to conduct a meta‐analysis. This might suggest that rare attention has been paid to cortico‐subcortical connectivity in adult. Of course, it is also possible that few relevant positive findings have been found in the adult. This was consistent with previous case‐control structural studies of ADHD which showed small to medium effect sizes of subcortex for adolescence and adult.Further, previous studies have shown that this developmental change in connectivity is associated with changes in symptoms of ADHD. Cortico‐striatal‐thalamo‐cortical (CSTC) loops are consistently implicated in many mental diseases.4.1In the present study, based on our assumption, we conducted a meta‐analysis from a new perspective of neurodevelopment. Besides, the results were well validated by our big dataset. Nevertheless, our results should be interpreted in light of some limitations.Firstly, heterogeneity needs to be taken into account when interpreting our results. ADHD is a highly heterogeneous disease. An increasing number of studies have indicated individual differences in brain development.Secondly, as with other meta‐analyses, we did not have sufficient numbers of original studies for more subgroup analysis in this study. At least 17–20 experiments were recommended to have sufficient power in ALE meta‐analyses,Finally, our analysis was based on cross‐sectional studies. It has been known that part of childhood patients would remit in adulthood. To wit, children with ADHD consist of both persistent ADHD and remit ADHD in adulthood.5The present work assessed the developmental changes of aberrant brain connectivity in ADHD. We found more consistent activation when the studies were grouped by patients' age. Specifically, there were obvious differences between cortex and subcortex, and between younger (<12 years) and older (≥12 years). Besides, the abnormal cortico‐cortical and cortico‐subcortical functional connectivity in children and abnormal cortico‐cortical functional connectivity in adults were verified in our independent database. Our results emphasize the importance of age effect when studying the brain function of ADHD. We proposed that cortico‐cortical and cortical‐subcortical connectivity might be an essential aspect in the pathophysiology of children with ADHD, while abnormal cortico‐cortical connections were more important for older ADHD (mainly for adults with ADHD). This work allows us to better understand neurodevelopmental mechanisms of ADHD, which hold important implications for future prediagnosis and clinical treatment.This work was supported by the National Science Foundation of China , the Capital's Funds for Health Improvement and Research , and the National Key Basic Research Program of China (973 program 2014CB846104).The named authors have no conflict of interest, financial or otherwise.AppendixS1Click here for additional data file.


Attention Deficit Hyperactivity Disorder (ADHD) is characterized by harmful levels of inattention, hyperactivity, and impulsivity and occurs in 2.5% of adults.This project will evaluate young adults with ADHD in computerized tasks that assess different forms of attention and inhibition, correlating them with self-report scales and physiological measurements (EMG and fNIRS) to identify impairments in specific cognitive domains.The study will be conducted with two groups: one with ADHD - GClin and one control - CG, with 50 participants between 18 and 28 years each. Initially, participants will perform CPT-3 and respond to ASRS to be allocated to the CG or GClin, with validation by a specialist physician. After that, they will do the computerized inhibition (Stroop / Stop) temporal and spatial attention (voluntary and automatic) tests. In this phase the data will be collected using electromyographic measurements and recording of brain activity in areas of the prefrontal and temporal cortices through fNIRS. After the tests they will complete the impulsivity scales (BIS-11 and UPPS). The analyzes will comprise: (1) ANOVA of the means of TRs and the accuracy of the computerized tests; (2) Correlation analysis of RT, accuracy and ASRS scores; and (3) The fNIRS analysis will use the oxyhemoglobin signal, which will be analyzed individually.As expected results there will be differences between CG or GClin in relation to impulsiveness, number of errors and brain activation.The integration of physiological measurements, scales and tests will ensure integrated understanding of attentional and inhibitory processes impaired in ADHD.No significant relationships.


Prior published analyses of the same dataset have already show that alpha event-related desynchronization (ERD) in the hemisphere contralateral to unimanual tapping is related to hyperactive/impulsive symptom severity  but not to inattentive symptom severity. Our findings demonstrate a neurobiological dissociation in ADHD symptom severity, with implications for understanding the structure of endophenotypes in the disorder as well as for biomarker development.Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders characterized in current diagnostic criteria by two dominant symptoms, inattention and hyperactivity/impulsivity. Here, we show that task-related alpha (8–12 Hz) interhemispheric connectivity changes, as assessed during a unimanual finger-tapping task, is correlated with inattentive symptom severity ( Attention-deficit/hyperactivity disorder (ADHD) affects approximately 5% of children worldwide , resultiUnder DSM-5, there are three explicit presentations of ADHD: predominantly inattentive (ADHD-I), predominantly hyperactive/impulsive (ADHD-H/I) and combined presentation (ADHD-C). Questions exist as to how inattentive and hyperactive/impulsive symptoms relate to each other. One possibility is that the two symptom clusters reflect different manifestations of the same biology. For example, the ADHD-H/I presentation is uncommon in school-age children  and is mr = 0.90)  . Modelin = 0.90) . These o = 0.90) . It ther = 0.90)  or repreNeuroimaging offers a method for external validation of the structure of cognitive mechanisms . Clear dr = 0.43, p = 0.04)  . In the The participants were identical to those reported in our previous studies . Fifty cp = 0.062, Cohen’s d = 0.54), all analyses were adjusted for IQ.The ADHD Rating Scale  was usedvia a video monitor. There were five blocks consisting of 20 trials per block, and each trial lasted 6 s, with a one-second rest/baseline period prior to the “Go” cue in each trial. LHFT and RHFT trials alternated within block. The first trial was LHFT or RHFT randomly assigned among participants, and the number of trials of LHFT and RHFT were equal. Behavioral overflow was computed by averaging the cumulative angular deflection of the non-tapping hand for all trials across blocks together at the same time during LHFT and RHFT.Motor overflow was measured and quantified using the TSD131 finger twitch transducers . Participants were required to successively tap each finger against the thumb with self-paced timing in a fixed order (index-middle-ring-little), with alternating trials of left-handed (non-dominant) finger tapping (LHFT) and right-handed (dominant) tapping (RHFT). A start cue was given EEG data analysis pipeline is shown in To examine the physiological changes underlying behavior, we performed EEG functional connectivity analyses on the source level using the Brainstorm toolbox . First, t-tests between ADHD and TD groups to examine potential differences in demographics, behavioral mirror overflow (via goniometer) and clinical data (Conners ADHD Inattentive and Conners ADHD Hyperactive/Impulsive). All t-tests were two-sided, with α = 0.05. To examine the relationship between ADHD symptom severity and interhemispheric functional connectivity, we calculated Pearson’s r independently in the ADHD and TD group. GAI was used for IQ statistical adjustment, as is typical in ADHD literature, because it is designed to be insensitive to working memory and processing speed differences; these cognitive traits are considered to be key aspects of the ADHD phenotype. We also adjusted for age and sex in the statistical analyses.We ran independent-samples p = 0.31, Cohen’s d = 0.29), sex , and GAI  between the ADHD and TD groups. Moreover, there were no significant differences in the behavioral motor overflow during (dominant) RHFT . However, compared to TD controls, children with ADHD showed significantly more motor overflow during (non-dominant) LHFT, with a large effect size .As shown in p = 0.14, Cohen’s d = 0.45).Children with ADHD did not show decreased task-related changes in interhemispheric functional connectivity between the left and right sensorimotor areas in alpha during LHFT in comparison with TD controls, though the within-sample difference was of a moderate effect size ; whereas there was no such significant correlation among TD controls . We did not find significant correlation between ipsilateral alpha ERD and functional connectivity in either children with ADHD or TD controls.As shown in r = −0.10, p = 0.64), whereas there was a significant correlation between connectivity and overflow in TD controls .Children with ADHD did not show a significant correlation between functional connectivity and mirror overflow ; this relationship persisted even when adjusting for contralateral ERD (p = 0.038). There were no significant correlations between interhemispheric functional connectivity and Conners inattentive in TD controls . Moreover, no significant correlation was found between interhemispheric functional connectivity and Conners ADHD H/I symptoms in either children with ADHD  or in TD controls .As shown in r = 0.55, p = 0.01) but not with H/I symptom severity . This association persists even when adjusting for correlations between connectivity modulation and ERD. By contrast, we had previously identified that among children with ADHD, intra-hemispheric alpha ERD correlates with ADHD H/I symptoms but not with inattentive symptoms (Our primary finding was that finger-tapping-task-related interhemispheric connectivity modulation correlated with Conners ADHD inattentive symptoms in children with ADHD during non-dominant finger tapping (LHFT)  differences in connectivity modulation. However, a couple of findings were not completely consistent with falsifying the role of transcallosal communication in the production of mirror overflow: First, given the “small to moderate” effect size of d = 0.45 (despite inability to reject the null), this negative finding may represent an underpowered inference and should be tested in a larger sample. Additionally, the TD group, while showing considerably less overflow than the ADHD group, did demonstrate a significant correlation between magnitude of overflow and task-related functional connectivity modulation, supporting a role of transcallosal signaling in progressive disinhibition of mirror overflow movements across child development.Separately, in prior papers , we haveSeveral limitations need to be considered in interpreting our primary conclusion, which is the identification of a dissociation between EEG correlates of H/I symptom severity and Inattentive symptom severity. First, a wider range of tasks, recording modalities and literature-guided analyses would be helpful in better examining biological dissociations between H/I and Inattentive symptoms, as would a wider age- and severity-range of participants. Additionally, the sample size is relatively small. While the number of participants meets the requirement of minimum sample size, a larger sample would increase statistical power. If, for example, a further study were to have greater statistical power, group differences in task-related connectivity modulation may indeed become apparent, allowing for more nuanced examination of the still high plausible role of interhemispheric communication in generating mirror overflow. Finally, given that both mirror overflow and the symptoms of ADHD are phenomena with developmentally sensitive courses, and longitudinal data will be critical to understanding how biology and phenomenology co-evolve.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 Institutional Review Board of Johns Hopkins Medicine. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.YL: methodology, data analysis, and writing original draft. JA and DC: investigation, data acquisition, and data curation. SM: funding acquisition, project administration and reviewing, and editing of the manuscript. JE: conceptualization, supervision and reviewing, and editing of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The traditional diagnosis of Attention Deficits/Hyperactivity Disorder (ADHD) is through parent-child interviews and observations; therefore, innovative ADHD diagnostic tools that represent this digital era are needed. Virtual reality (VR) is a significant technology that can present a virtual immersive environment; it can provide an illusion of participation in an artificial milieu for children with ADHD. This study aimed to develop an ADHD-VR diagnostic tool construct (Research Domain Construct/RDC) based on the DSM5 ADHD diagnostic criteria, and using the RDC to develop a diagnostic tool with a machine learning (ML) application that can produce an intelligent model to receive some complex and multifaceted clinical data . We aimed to expand a model algorithm from the data, and finally make predictions by providing new data (output data) that have more accurate diagnostic value. This was an exploratory qualitative study and consisted of two stages. The first stage of the study applied the Delphi technique, and the goal was to translate ADHD symptoms based on DSM 5 diagnostic criteria into concrete behavior that can be observed among children in a classroom setting. This stage aimed to gather information, perceptions, consensus, and confirmation from experts. In this study, three rounds of Delphi were conducted. The second stage was to finalize the RDC of the ADHD-VR diagnostic tool with ML, based on the first-stage results. The results were transformed into concrete activities that could be applied in the programming of the ADHD-VR diagnostic tool, followed by starting to input data that were required to build the diagnostic tool. The second stage consisted of more than ten focus-group discussions (FGDs) before it could be transformed into the ADHD-VR diagnostic tool with the ML prototype. First-stage data analysis was performed using Microsoft Excel for Mac. Qualitative data were analyzed using conceptual content analysis with a manifest/latent analysis approach. From the first stage of the study, there were 13 examples of student behaviors that received more than 75% totally agreed or agreed from the experts. The RDC of the ADHD-VR diagnostic tool with machine learning application consisted of three domains and was divided into six sub-domains: reward-related processing, emotional lability, inhibitory, sustained attention, specific timing of playing in order, and arousal. In conclusion, the results of this study can be used as a reference for future studies in a similar context and content, that is, the ADHD-VR diagnostic tool with machine learning based on the constructed RDC. The Fourth Industrial Revolution (FIR) and digital revolution era have grown significantly over the last 10 years. The characteristics of this meaningful period are shown by disruptive technologies that can associate with the physical, digital, and biological aspects of the environment, for example, in health systems and services. The expanded usage of 4.0 technologies is expected to increase the precision, efficiency, and effectiveness of medical diagnostic tools, treatment approaches, and health monitoring to anticipate the influence of many diseases  is one of the most significant and innovative tools that encompasses FIR technologies in medical practices, especially in child and adolescent psychiatry in which (i) diagnostic approaches mainly depend on parents’ report questionnaires, psychiatric interviews, and observations; and (ii) treatment consideration has been limited to interpersonal psychotherapy and medication. Based on several studies, VR is defined as real-time interactive graphics with 3D or 4D models that construct the objective of a virtual immersive environment, thus giving an illusion of participation in an artificial milieu rather than a real external observation of such a situation . The VR Attention Deficits/Hyperactivity Disorder (ADHD) is a prominent mental health problem, especially among primary school children, and its worldwide prevalence is estimated to be 5 – 13% , 7. TherConsequently, developing an ADHD diagnostic tool based on ADHD clinical symptoms is important because it may complete and validate ADHD diagnosis based on the traditional approach . SeveralMoreover, machine learning (ML) applications produce an intelligent model that can receive complex and multifaceted clinical data , expand a model algorithm from the data, and finally make predictions by providing new data (output data) that have more accurate diagnostic value . TherefoTherefore, this study was designed to build an ADHD-VR diagnostic tool construct (Research Domain Construct/RDC) based on the DSM5 ADHD diagnostic criteria, and to use the RDC to develop an ADHD-VR diagnostic tool with a ML model. RDC may explain the correlation between several domains of ADHD symptoms and basic engineering principles to develop a dynamic software construction into ADHD-VR diagnostic tool platforms , 24. BasThe study design followed the first and second steps of the four-step method of ADHD VR diagnostic tool development . This stThe first stage of the study applied the Delphi technique, and the goal was to translate ADHD symptoms based on the DSM 5 diagnostic criteria into concrete behavior that can be observed among children in a classroom setting. This stage aimed to gather information, perceptions, consensus, and confirmation from experts. There were three rounds of Delphi and involved seven experts consisting of: (ii) three child psychiatrists, (ii) two child psychologists, and (iii) two school counselors who also acted as primary school teachers. The first and second rounds were designed as qualitative assessments to gather information and perception about the DSM 5 ADHD diagnostic criteria that can be observed among primary school student behaviors in classroom settings. The third round was designed as a quantitative assessment to obtain consensus or confirmation among experts according to the second-round results. Because of the coronavirus disease 2019 (COVID-19) pandemic, each round of the Delphi method was completed by sending the questionnaire through the participants’ email. The results of each round were sent to all the participants using questionnaires until a consensus was reached.The second stage was to finalize the RDC of the ADHD-VR diagnostic tool with ML, and followed by developing the ADHD-VR diagnostic tool prototype. Through the FGDs on the second stage, the consensus from the first-stage was transformed into RDC based on Baroni and Castellanos  and BenyIn the first stage of the study, a specific questionnaire was developed to build the RDC of the ADHD-VR diagnostic tool. In the first round, the study provided a questionnaire that could facilitate experts to express their perspectives and opinions on primary school student activities that may be observed in classroom settings based on the DSM 5 ADHD diagnostic criteria for inattentive, hyperactivity, and impulsivity symptoms. The questionnaire consisted of open-ended questions that asked, “Can you describe these symptoms based on your observation of the attitude and behavior of your patients/students with ADHD in the classroom setting?” The second-round questionnaire was developed based on the first-round results and performed as a table of statements. In the second round, experts answered the statements scored on a Likert scale: totally agree – 4, agree – 3, somewhat agree – 2, and disagree – 1. In addition, the second-round questionnaire was also included with comment columns so that experts could provide their feedback and judgments on the statements, if necessary. The last round questionnaire was built from the second-round results, inputs were modified, experts’ comments were followed, and statements that were rated as somewhat agree or disagree were excluded. In the third round, experts were invited to answer the statements on a Likert scale once more, and it was categorized as totally agree – 4, agree – 3, somewhat agree – 2, and disagree – 1. The final round was the consensus and confirmation phase. The level of consensus used in this study was 75% of agree or totally agree statements.In the first stage, the data analysis was performed using Microsoft Excel for Mac. Qualitative data were analyzed using conceptual content analysis with a manifest/latent analysis approach that consisted of four steps: decontextualization, recontextualization, categorization, and compilation . The conThe expert consensus is shown in This study aimed to develop an ADHD-VR diagnostic tool using ML, which consists of three stages, including feasibility testing. The conceptual content analysis from the first and second stages resulted in the ADHD-VR diagnostic tool prototype with an ML construct and general agreement with the ADHD-VR diagnostic tool prototype.From the first and second rounds of Delphi, the experts identified more than 50 examples of students with ADHD behavior in classroom settings based on 18 items of the DSM 5 ADHD diagnostic criteria. However, in the third round of Delphi, expert consensus showed only 13 examples of student behavior that could be represented by the DSM 5 diagnostic criteria for ADHD with 75% totally agree or agree statements .The focus group discussions during the second stage were based on the first-stage results. The FGDs results were generated by content conceptual analysis that consisted of decontextualization (Identify meaning units of each expert input and statement), recontextualization (Include inputs and statements that related to the research content), categorization (Identify the similarities of each statement and put into homogenous groups), and compilation (All inputs and statements compiled and concluded) to finalize the RDC of the ADHD-VR diagnostic tool. Based on the RDC and other compiled inputs, it was transformed into concrete activities that applied in the programming of the ADHD-VR diagnostic tool prototype with ML application.Schedule setting component, consisted of the following:a.Reward-related processing aims to identify inattentive and hyperactivity-impulsivity symptoms. This subcomponent may be related to alterations in the dopaminergic and serotonergic systems of the ADHD brain. Therefore, activity is designed to examine the release of such neurotransmitters, such as dopamine and serotonin. Hence, a role-playing activity with challenging tasks was chosen (acting as a student in the classroom). The participants were instructed to complete several challenging tasks using visual and auditory instructions  . Every tb.via chatting or personal data exchange, no data showing the points that were collected during training, and no data related to playing time, and more.Emotional lability subcomponents are meant to quantify hyperactivity-impulsivity symptoms, especially those associated with emotion-related symptoms such as restlessness, difficulty in engaging in leisure activities, and difficulty in waiting turns. Thus, the challenging tasks are designed in several ways, such as objects  simultaneously appearing, using two types of instruction , time limitation, and only using a nice sound but not other rewards as signs that students correctly or incorrectly completed their tasks. In addition, they cannot interact Immediate feedback component, consisted of the following:a.The inhibitory subcomponent is arranged to identify inattentive, hyperactivity-impulsivity symptoms, and working memory. This arrangement is closely related to self-monitoring skills and goal-directed behaviors related to impulsive and hyperactive symptoms in children and adolescents with ADHD . The ADHb.The sustained attention subcomponents include organizational skills and working memory. The ADHD-VR diagnostic tool construct used a digital technology that tracked children and adolescent sustained attention skills by recording their ability to correctly memorize the specific tasks, when it was coupled with both auditory and visual distractors  that appeared simultaneously in real-time. Before and during task completion, participants need to sustain their attention, memorize the instructions , and choose the right objects that appear concurrently. Meanwhile, they also need to organize their own strategies to complete their tasks. Thus, this immersion environment that is delivered through VR screens may quantify the participants’ ability to be selectively attentive to task completion, and also to organize their behavior in order to follow the given instruction . The sofDifficulty matching component, consisted of the following:a.Specific timing of playing to identify the participant’s abilities to control and organize their behavior. The ADHD-VR diagnostic tool prototype was designed to recognize participant time management skills associated with inattention, hyperactivity-impulsivity, self-organization skills, and inhibition symptoms. Therefore, the total duration of the ADHD-VR diagnostic tool was designed for approximately 30 min of playing time, including a tutorial part. The faster they correctly completed the instructions, the better their motor timing, perceptual timing, and temporal foresight skills. Meanwhile, it also showed better attention span and self-organization skills.b.The arousal subcomponents were designed to identify the participant’s ability to control distractions during task accomplishment. Once the participants correctly complete a task, the tool makes a nice sound that may or may not keep them aroused during the examination. In addition, it may or may not trigger the initiation skills. Therefore, this approach may also quantify inattentive and hyperactivity-impulsivity symptoms.The final compilation of the experts’ inputs during FGDs and based on the RDC, then it was applied and programmed into ADHD-VR diagnostic tool prototype with ML application, as follows:1.The ADHD-VR diagnostic tool prototype is a serious game that can adjunctively help professionals diagnose ADHD and uses the DSM 5 ADHD diagnosis criteria as a gold standard. This tool is intended for health-use purposes; therefore, it resulted in the game being used under supervision, thus differing from the usual daily game applications.2.The ADHD-VR diagnosis tool prototype consisted of Indonesian local content and culture and was designed as a simulation 360-degree game.3.The ADHD-VR diagnosis tool prototype can be used for school-aged children.4.The concept of the ADHD-VR diagnostic tool is to immerse students in a virtual classroom. Participants can look around in every direction, interact with virtual objects  using controllers that emulate the user’s hand, and walk anywhere as long as they are within the limits allowed by the game. To emulate the real world, the visual appearance of virtual objects and surroundings needs to be as close as possible to real objects, although the limit would be within the processing power of the VR equipment itself. The general direction for the ADHD-VR diagnostic tool was to create a local Indonesian elementary classroom, something that is familiar for the participants especially if this was their first time entering the virtual world and to give a sense of “task” for the participants as if he/she was given an instruction from the teacher. The room area is approximately 5 × 5 m, while the game area is approximately 3 × 3 m. The game area is the area where collectible objects appear.5.In the ADHD-VR diagnostic tool, the participant’s point of view is from the eye of a virtual character (student) rendered inside the VR headset/oculus . This po6.The ADHD-VR diagnostic tool uses the Oculus Rift headset with hand controllers .7.The ADHD-VR diagnostic tool was created using the Unity application, which will be converted into an a.apk file and installed into Oculus. The game data will be stored in the MongoDB database. Currently, ML application is separated from VR applications. Data from the ADHD-VR diagnostic tool is stored in the database, from the database it is processed using ML application.8.The total duration of the ADHD-VR diagnostic tool examination is approximately 30 min, which consists of two levels of playing .9.In this study, an ML application much more fitting for small datasets was designed with low complexity to avoid overfitting the model to the data. Thus, the study chose a simpler classifier model, such as the “decision tree,” as a predictive model for small datasets; it is easy to understand and interpret, perfect for visual representation, requires little data preprocessing, can work with numerical and categorical features, and can be used for regression  or classification . This study focused on the classification output based on the DSM 5 ADHD diagnostic output . The classification and regression tree (CART) algorithm was used for classification tree construction, which is based on binary splitting of the attributes. CART uses’ Gini’ impurity index splitting measure. The CART algorithm was selected because it can handle both numerical and categorical data and multi-output problems, requires relatively little effort for data preparation, and non-linear relationships between parameters do not affect tree performance .10.The ADHD-VR diagnostic tool procedure:a)The participant is explained how to use the ADHD-VR diagnostic tool before he/she starts the examination, such as being supervised, act as a student in the classroom, tasks that need to be completed, and duration of the examination, and when they feel dizzy, tired, or have any other complaints, they can stop the examination at any time.b)The ADHD-VR diagnostic tool examination starts when the participant turns on the Oculus Rift and the menu appears. The participants were instructed to choose the tutorial button that appeared on the oculus screen. In this part, the participant is asked to practice the hand controller unit, grab, and place the object. The participant is asked to put one object  into the box according to the visual instructions that appear in the classroom table. The tutorial level is completed when the participant can put the object correctly into the box at least eight times out of ten (80% correct). The tutorial level helps the participant become familiar with the Oculus Rift and hand controller unit. Although it has some learning points, it does not affect the examination results.c)After completing the tutorial, the participant can continue to the next level (examination level) by pushing the examination level button that appears on the oculus screen.d)At the examination level, the participant is asked to put objects into the box correctly according to instructions, such as putting three balls into the correct box, putting two balls and one book into the correct box, putting one ball and two books into the correct box, and putting three books into the correct box. They need to complete the instruction in order; if it is said to put two balls and one book into the box, it means that they need to put two balls first and later put the book into the box. There are two types of instruction at this level: visual (written on the whiteboard in front of the class) and auditory. The objects are defined as books (random size and color) and balls (random size and color). Objects may appear randomly in the virtual classroom and can be on the table or floor. The objects appear every 10 s. During the task completion, they need to remember the instruction while there are several distractions, such as auditory distractions  and visual distractions like tables and chairs in the classroom. The duration of the examination level is designed for approximately 20–25 min.e)After completing the entire level, the ADHD-VR diagnostic tool produces several data (parameters) that can be transferred into the ML application to predict the possibility of ADHD diagnosis. The parameters are:i.The total number of correct objects that are put in order into the box.ii.The total number of correct instructions that have been completed (in order).iii.The total number of incorrect objects that are put in order into the box.iv.The total number of incorrect instructions that have been completed (in order).v.The total number of objects that have not been put into the box after the game is over.vi.The total time duration between the instruction is given and task completion.vii.The total duration of time to remember each instruction .viii.The ratio between correct objects that are put into the box and the total objects that appear in the virtual classroom.ix.The ratio between incorrect objects that are put into the box and the total objects that appear in the virtual classroom.x.The total duration of time to put objects into the box after the instruction is given .xi.The total number of correct objects that are put into the box before the instruction is given completely.xii.The total number of incorrect objects that are put into the box before the instruction is completely given.The development of the ADHD-VR diagnostic tool with ML followed the RDC with the DSM 5 ADHD diagnostic criteria as a gold standard supposed to adjunct the ADHD diagnosis. VR was selected as the ADHD diagnostic tool because it can produce immersive experiences and interactive activities that push more vivid images for participants. The participants were put into the virtual classroom and it created a mental feeling such as sensation and perception of “being there” (“presence”). Thus, it might trigger a certain level of likelihood of interacting and reacting, as if the participants were in the real classroom. During the feasibility testing, participants mentioned that they felt as if they were in their real classroom and the only limitation was their restricted movement in the VR classroom, but it did not reduce their “presence” sensation. These experiences are very important in ADHD-VR diagnostic tools because a higher likelihood of reality experiences may be correlated with higher diagnostic levels , 31, 32.In this study, we developed an ADHD-VR diagnostic tool prototype using ML applications. The tool was designed based on the RDC, which attempted to produce a simple tool with minimal distractions that could detect ADHD symptoms. For example, the child used an Oculus Rift with a hand controller. Instead of experiencing an immersive virtual classroom, children can grab, release, throw, and place objects using their virtual hands. Additionally, the virtual classroom was designed in a limited space. There are several advantages of the design, such as its ability to limit physical movements; children only need a small physical space to operate the VR digital game, which allows them to perform actions while standing or sitting . FurtherThe whole design had diagnostic values such as children could grab and release objects from their surroundings and place them in the correct box within a “working space.” Based on the ConvNet theoretical background, the ADHD-VR diagnostic tool prototype contained several sequences of layers (activities) within which children could walk, grab, release, throw, and place objects that have diagnostic value. After the child grabbed/released/threw/placed an object from the toolbox, a new object of the same or different type would spawn to take its place. Thus, more than one layer of a given type could be present in the prototype. While computing, a display in front of the child reported the status of playing , 33. UpoSince traditional parents’ or teachers’ ADHD behavior rating scales have subjective components, additional or more objective assessments of childhood ADHD tools have been recommended . SeveralIn conclusion, this study developed an ADHD-VR diagnostic tool with ML based on a constructed RDC. This approach may be useful for other professionals who may also develop robust ADHD-VR or other VR diagnostic tools that are important in health services, especially mental health. Therefore, the results of this study can be used as a reference for future studies in similar contexts. The sole limitation of this study was that it did not include the effectiveness of the ADHD-VR diagnostic tool results. Thus, an effectiveness study needs to be conducted soon so that it can be applied in a clinical setting.The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics Committee of the Faculty of Medicine, University of Indonesia, Jakarta, Indonesia (reference number: KET-583/UN2.F1/ETIK/PPM.00.02/2019). 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) for the publication of any potentially identifiable images or data included in this article.TW contributed to the design of the manuscript, literature review, draft and preparation, and writing of the manuscript. NW contributed to the literature review and manuscript preparation. ST, RI, FK, RB, and BD conceptualized and reviewed the manuscript. All authors contributed to the article and approved the submitted version.


Attention-deficit/hyperactivity disorder (ADHD) is a relatively commonly occurring neurodevelopmental disorder affecting approximately 5% of children and young people. The neurobiological mechanisms of ADHD are proposed to particularly center around increased dopamine receptor availability related to associated symptoms of reduced attention regulation and impulsivity. ADHD is also persistent across the lifespan and associated with a raft of impulsive and health-risk behaviors including substance abuse and smoking. Research highlighting the potentially significant levels of monoamine oxidase (MAO) inhibitory properties in tobacco smoke and e-cigarettes may provide a mechanism for increased tobacco smoke dependence among those with ADHD, in addition to the role of nicotine.This scoping review aimed to establish evidence for the above neurobiological pathway between smoking and ADHD symptom-alleviation or “self-medication” with the inclusion of the mechanism of MAO-inhibitors indirect increasing dopamine in the brain.Scoping review methodologies were employed in this review selected to synthesize multiple sources of empirical research to identify current gaps in the knowledge base and identify key characteristics of research data related to a phenomenon. Databases searched included OVID MEDLINE(R), Embase, Cochrane, PsycINFO and SCOPUS limited to 2000 onward and empirically validated, peer-reviewed research.There is support for the role of MAO-inhibition on greater reinforcement of smoking for individuals with ADHD through a greater impact on dopaminergic availability than nicotine; potentially moderating ADHD symptoms.Greater support for a “self-medication” model of ADHD and smoking includes not only nicotine but also MAO-inhibitors as dopamine agonists contained in cigarettes and e-cigarettes. The disctioning . Tobaccoctioning  and is pctioning .Various explanatory models have been proposed in unraveling the relationship between ADHD and nicotine use. These include greater responsiveness among individuals with ADHD to socio-behavioral influences such as imitation and peer pressure . An earlThe potential interplay between self-medication models and socio-behavioral influences between ADHD and nicotine have been reviewed by This current scoping review seeks to further explore mechanisms of a self-medication model of ADHD and nicotine by including the possibly important role of monoamine oxidase inhibitory (MAOI) activity. Such an inclusion of MAOI activity is timely given the wider usage of e-cigarettes containing physiologically significant levels of MAO inhibitory activity  and the Dopamine release has been implicated as a key mechanism of ADHD symptomology. In particular, neurobiological models have postulated that atypical properties of dopamine release impact the reward processing pathway of individuals diagnosed with ADHD . NeuroimThe dopamine transfer deficit (DTD) model proposed by A scoping methodology has been selected for this review as it follows the suggestions of This review seeks to further explore mechanisms of a self-medication model of ADHD and nicotine by including the possibly important role of monoamine oxidase inhibitory (MAOI).1.Is there evidence for the role of monoamine oxidase inhibitory (MAOI) compounds influencing aberrant striatal dopaminergic systems in ADHD and dopamine enhancing effects of cigarettes ?Studies were identified using OVID MEDLINE(R), Embase, Cochrane, PsycINFO and SCOPUS searches. Boolean operators were employed with terms such as: “nicotine, cigarette, smoking tobacco, vaping, e-cigarette; ADHD, attention-deficit, hyperkinetic and dopamine, monoamine oxidase, MAO, or MAO-inhibition.”Both animal and human research studies were included. Randomized or non-randomized studies were included, as well as prospective or retrospective naturalistic studies.In order to maintain current relevancy, the time period of focus was 2000 onward in order to build on the growing resource of reviews of research and literature completed since 2000. Exceptions to this time-frame included studies that were foundational to the topic. Empirically-validated, peer-reviewed research were the focus. Gray research such as policy papers and evaluative research were excluded.To be included in the analysis studies must have included information regarding effect size  and information relating to methodologies employed. Studies were excluded due to lack of outcome measures or insufficient data relating to methodology.n = 5934) were initially screened by one author through review of titles and if needed abstracts, to ascertain pertinence to the subject area. The same author reviewed the abstracts of the remaining studies (n = 557) to extract methodological information and statistical information. These studies were then reviewed by two authors to assess eligibility from abstracts and full-text articles for the review of strength of evidence (n = 34). See Eligible studies  . This islability  and thatlability .The above scoping review demonstrated promising evidence for the additional (or even primary) role of MAO-inhibitory compounds in cigarettes and e-cigarettes in greater vulnerability to smoking abuse and dependence among individuals with ADHD. This hypothesis is based on dual factors: evidence to suggest that MAO-inhibitors in cigarettes and e-cigarettes may have a stronger effect on dopaminergic systems than nicotine  and thatA well-addressed limitation of scoping reviews is the lack of clarity around strength of evidence . FurtherAll authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.