Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are neurodevelopmental disorders that exhibit within-disorder heterogeneity and cross-disorder phenotypic overlap, thus suggesting that the current disease categories may not fully represent the etiologic essence of the disorders, especially for highly comorbid neurodevelopmental disorders. In this study, we explored the subtypes of a combined sample of ASD and ADHD by integrating measurements of behavior, cognition and brain imaging.A total of 164 participants, including 65 with ASD, 47 with ADHD, and 52 controls, were recruited. Unsupervised machine learning with an agglomerative hierarchical clustering algorithm was used to identify transdiagnostic symptom clusters. Neurocognition and brain structural connectivity measurements were used to assess symptom clusters. Mediation analysis was used to explore the relationship between transdiagnostic symptoms, neurocognition and brain structural connectivity.We identified three symptom clusters that did not fall within the diagnostic boundaries of DSM. External measurements from neurocognition and neuroimaging domains supported distinct profiles, including fine motor function, verbal fluency, and structural connectivity in the corpus callosum between these symptom clusters, highlighting possible biomarkers for ASD and ADHD. Additionally, fine motor function was shown to mediate the relationship between the corpus callosum and perseveration symptoms.In this transdiagnostic study on ASD and ADHD, we identified three subtypes showing meaningful associations between symptoms, neurocognition and brain white matter structural connectivity. The fine motor function and structural connectivity of corpus callosum might be used as biomarkers for neurodevelopmental disorders with social skill symptoms. The results of this study highlighted the importance of precise phenotyping and further supported the effects of fine motor intervention on ASD and ADHD.The online version contains supplementary material available at 10.1186/s12888-022-03734-4. Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are neurodevelopmental disorders with symptoms starting to manifest in early childhood . ASD conThe symptom-based categorical systems of mental disorders are regarded as the gold standard for diagnosing neurodevelopmental disorders. However, children with ASD or ADHD usually do not entirely fit within the boundaries of a single disorder; instead, they show a mixture of clinical manifestations . For insThe complex interaction of genetic and environmental factors may contribute to the development of these neuropsychiatric disorders . With a Deficits in neurocognitive function have been found in both ASD and ADHD , 16. PreNumerous studies have reported abnormal brain structure and function in these two neurodevelopmental disorders , 22. AltExisting studies focusing on ADHD and mood disorders supported the application of the RDoC framework concept in child and adolescent psychiatry –34. Few Previous studies have reported several main pathways linking neuroimaging or neurocognition with neurodevelopmental disorders. First, neuroimaging abnormalities have been suggested as a candidate endophenotype of ASD and/or ADHD, which directly affects symptoms , 22, 39.This study aimed to establish the dimensional symptom subtyping of a combined sample of ASD and ADHD by using a transdiagnostic approach to explore the overlapping neuroimaging and neurocognitive etiology of these two neurodevelopmental disorders. We selected social communication difficulties and inattention/hyperactivity symptoms as the classification inputs for unsupervised machine learning to determine naturally occurring transdiagnostic clusters in a representative combined sample of ASD and ADHD and typically developed children. We hypothesized that children might have symptom clusters, which might not always be within the diagnostic scope, and the symptom clusters could be mapped to specific neurocognition and brain structural connectivity profiles. For instance, abnormalities in the corpus callosum and/or frontoparietal white matter connectivity could be associated with social skill symptoms, which might determine the correlation of EF with ASD/ADHD symptoms. Additionally, we aimed to explore the interrelationships among symptoms, cognition and brain structural connectivity by using a mediation effect model.Participants diagnosed with ASD or ADHD were recruited from the West China Hospital outpatient and schools in the local community. Parents/guardians of participants provided the written informed consent for study participation, which was approved by the Medical Ethical Committee of West China Hospital of Sichuan University. Clinical diagnoses were confirmed by experienced child psychiatrists based on DSM-5 and were supported by questionnaires, structured interviews and direct observations. Autism Diagnostic Observation Schedule-General (ADOS-G)  and AutiN = 65; ADHD, N = 47; TDC, N = 52). ADHD subtypes included inattention (N = 24), hyperactivity/ impulsivity (N = 10) and combined types (N = 13).All participants obtained K-SADS-PL evaluation. Only patients who were suspected of ASD completed ASD diagnostic evaluation. All parents/guardians of participants completed AQ and SNAP-IV to evaluate symptoms of ASD to ADHD in children, including socialness, mindreading, patterns, attention to details, perseveration (attention switching); inattention (IA), hyperactivity/impulsivity (HI), and oppositional defiant disorder (ODD) symptoms (details of the measures were shown in the supplementary data). After the evaluation, subjects who met the standard were required to perform the cognitive tests: Chinese-Wechsler Intelligence Scale for Children Third Edition (C-WISC-III) , Develop3). DTI scans were acquired using a two-dimensional diffusion-weighted echoplanar imaging sequence .All brain imaging was performed on a 3.0-T imaging system  at the Tibet Chengban Branch of Sichuan University West China Hospital. Three-dimensional T1 images were acquired using a spoiled gradient recalled echoing planar imaging sequence  [p < 0.05, with TFCE FWE fully corrected for multiple comparisons.Data were analyzed using the FSL software (slwiki/) . Qualityslwiki/) . For imaslwiki/)  was condStatistical analyses were conducted using Jupyter, scikit-learn, NumPy, SciPy, and matplotlib packages . Unsupervised machine learning was adopted to classify children into clusters. Z scores were applied for cognitive profiles measured in different metrological units. The inverse values were inverted to facilitate the creation of a numerical matrix, e.g., SWM and IED scores. Eight subscale Z-scores of AQ and SNAP-IV  for all 164 children were input into the agglomerative hierarchical clustering to generate clusters. With stability to generate clusters even in small samples, agglomerative hierarchical clustering was performed. The optimal number of clusters was determined using two standard methods including the gap statistic  and the One-way analysis of variance (ANOVA) was used to compare ASD, ADHD and control groups, followed by Bonferroni multiple-comparisons test. We evaluated how the clusters differentiated on the symptoms, neurocognition, and microstructural connectivity by ANOVA analysis with post-hoc Bonferroni correction. To provide a complementary diagnostic frame of reference for interpretation, the symptom clusters were mapped onto the DSM diagnostic categories. Pearson correlation was used to explore the association among FA values, cognitions, and clinical symptoms. Statistical analyses were performed using SPSS .Finally, a mediating effect model was constructed using Amos version 21.0 to examine how cognition affected the relationship between brain structural connectivity and the symptoms. The following indexes were used to check model fit : a) rootP > 0.05). Although there were differences in IQ among groups , all participants had a IQ > 70 . Considering the imbalanced IQ distribution among groups, IQ was set as a confounder in the ANOVA analysis. Results showed differences among groups in PPT, VP, MC, VF-se, SOC, and SWM. Post hoc t tests revealed differences between ASD and ADHD in PPT, MC, and SWM  with respect to age, sex, maternal infection during pregnancy, birth, neonatal diseases, and parental education levels , relatively normal children , or inattention/hyperactivity/impulsivity symptoms  , which suggested that clusters did not represent the original diagnosis . Semantic verbal fluency in C1 was worse than that in C3 . There were no significant differences in other cognitions among clusters , cognitive skill-like motor function and verbal fluency. The body of the corpus callosum  was found to be linked with perseveration symptoms  and PPT-hands . The FA of the internal capsule (C1 < C2) was correlated with mindreading symptoms , and was slightly correlated with fine motor function , planning , sustain attention  , the SOC was negatively correlated with SNAP total  , IA/HI symptoms  and the corpus callosum , the corpus callosum was correlated with the perseveration symptoms . Moreover, the mediating effect model among the corpus callosum brain structural connectivity, fine motor function, and perseveration showed adequate goodness of fit . The corpus callosum structural connectivity predicted the perseveration symptoms , while a direct path from corpus callosum to perseveration was not significant   were determined by using agglomerative hierarchical clustering, which were not mapped onto the DSM categories. This study indicated white matter variations among subtypes. Our results also revealed that the C1 subtype was distinct from the C3 subtype in fine motor dysfunction and verbal fluency. Neuroimage-neurocognition-transdiagnostic symptoms interactions were investigated via correlation analysis, and the results revealed an association between WM structural connectivity, motor functions/verbal fluency/EFs, and ASD/ADHD symptoms. To the best of our knowledge, this was the first study to identify fine motor function as a mediator for the corpus callosum structural connectivity and perseveration symptoms. Our findings provided an opportunity for a more biomarker-based approach for disease subtyping, evidenced on precise phenotypes of a large category of neurodevelopmental disorders including ASD and ADHD.C1 subtype had fine motor dysfunction, which was consistent with previous within-diagnosis research in ASD and ADHD , 59. OurOur study also indicated dysfunction of verbal fluency in the C1 subtype, thus suggesting that deficit in verbal fluency was associated with impairment in social skills in ASD and ADHD. This result was partly in line with previous studies, which reported that ASD is associated with various semantic abnormalities . FurtherAs for other cognition profiles of the three clusters, we failed to identify any differences in EF among the three symptom subtypes, which was somewhat surprising considering that specific EFs were shown to be linked with ASD and ADHD in a number of studies , 67. In In the present study, three subtypes showed distinct neuroimaging profiles. Firstly, when comparing C1 with C3, C1 was characterized by decreased structural connectivity in the body of the corpus callosum. The FA values showed differences in the body/splenium of the corpus callosum when comparing C2 with C3. The symptom dimensional analysis showed that the corpus callosum (C1 vs. C3) was associated with mindreading and perseveration symptoms, while the corpus callosum (C2 vs. C3) was associated with ADHD symptoms. This suggested that the corpus callosum played a role in the overlapping of ASD-ADHD spectrum disorders. Consistent with our findings, a dimensional analysis showed that ASD symptom severity and inattention symptoms were associated with FA of the corpus callosum across ASD and ADHD . HoweverWe found decreased FA within the internal capsule in the C1 subtype compared with C2, and the internal capsule was correlated with mindreading symptoms. The result was aligned with previous studies that indicated social communication was associated with internal capsules in ASD , 74. AddThe existence of three independent symptom clusters was statistically justified, which showed distinct symptom profiles cutting across DSM diagnostic categories. Yet, the discriminant analysis results showed good predictive accuracy 92%) of the symptom dimensions, thus indicating that the clustering analysis result was reliable. There was no difference in two typical autism repetitive and stereotyped behaviors (RBBs) symptoms: patterns and attention to detail, which might be explained by the overlapping phenotypic characteristics of ADHD and ASD 2% of the, 75. Ste. As we proposed we have found associations between the corpus callosum with sensorimotor functions, which is consistent with previous studies [The present study indicated several links among WM structural connectivity, cognitions, and ASD/ADHD symptoms. Nevertheless, determining how neuroimage is linked to ASD/ADHD symptoms and how cognition may be candidate endophenotypes for neurodevelopment disorders, including ASD and ADHD, is challenging. A simplified schematic form for neuroimaging-cognition endophenotypes- symptoms of ASD and ADHD are depicted in Fig. 6 studies , 74. FinOur study successfully established one mediation model, which indicated that fine motor function had a total mediating effect between the corpus callosum and perseveration symptoms in a combined sample of ASD and ADHD. Previous studies showed that the microstructure of the corpus callosum was associated with motor function , and finThis study had a few limitations. First, the sample size was relatively small, which might lead to overfitting. Thus, future studies with larger samples are in need to generate more subtle neurodevelopmental disorders subtypes. However, the validity of the clustering analysis had been validated to be robust in our data with parameters, such as, silhouette score, classification accuracy, adjusted Rand score and so on. Additionally, we also added neuroimaging and cognitive tests to validate the generated subtypes. Secondly, we included only high-functioning ASD (IQ > 70), which may not represent the full spectrum of ASD. The inclusion criteria were made to balance the IQ among groups, to eliminate the influence of intelligence on cognition and white matter structural connectivity profiles of symptom clusters as possible. Finally, there was a possibility of selection bias, given that the present study participants incorporated several selective cognition tests assessing a number of cognitive domains. Further subtype validation is needed, such as longitudinal follow-up of the prognosis of different subtypes, using broader neuroimaging methods and neurocognitive testing. In our next study, we plan to employ machine learning analysis to explore brain and/or neurocognitive measurements subtypes in a larger sample.This study demonstrated the effectiveness of a transdiagnostic approach for identifying neurodevelopmental subtypes incorporating ADHD with ASD. Three subtypes of symptom profiles were identified and validated across neurocognitive function and brain structural connectivity domains. Fine motor function and structural connectivity in the corpus callosum might be essential in distinguishing ASD-related from ADHD-related symptoms. A fine motor function had a mediating effect on the linkage between the structural connectivity of corpus callosum and perseveration symptoms. This study provided evidence for precise phenotyping by integrating behavioral and neurocognitive measurements as well as brain imaging data, thus promoting the exploration of new targeted interventions for neurodevelopmental disorders such as ASD and ADHD.Additional file 1.


M = 13.6, SD = 2.7, range 5–18 years; 50.9% girls), including 294 (12.2%) with ADHD and 2,124 (87.8%) without ADHD. The proportion with prior concussion among youth with ADHD (43.9%) was significantly greater than youth without ADHD . A significantly higher proportion of boys with ADHD had a prior concussion history (48.1%) than boys without ADHD , but this difference was not observed for girls . Youth with ADHD did not differ with regard to their estimated longest duration of symptoms from a prior concussion  and the proportion who reported taking longer than 28 days to recover from a prior concussion did not differ between those with ADHD (15.3%) and without ADHD (12.2%), χ2 = 2.20, p = 0.14. Among youth presenting to a specialty clinic, ADHD was associated with greater lifetime history of concussion but not a greater duration of symptoms from a prior injury.Child and adolescent student athletes with attention-deficit/hyperactivity disorder (ADHD) report a greater lifetime history of concussion than those without ADHD. This case-control study compared youth with and without ADHD presenting for care at a specialty concussion clinic on their lifetime history of concussion. We hypothesized that a greater proportion of youth with ADHD would report a history of prior concussion. Archival clinical data from patients presenting to a specialty concussion clinic in Montreal, Québec, Canada between September 2015 and August 2019 were analyzed. The sample included 2,418 children and adolescents (age:  N = 10,585) from the U.S. general population ages 9 and 10 years old. Namely, the prevalence of prior concussion among children with ADHD was 7.2% compared to 3.2% among children without ADHD, meaning ADHD is associated with twice the odds of experiencing a prior concussion before age 11 (Numerous studies have found that children and adolescents with attention-deficit/hyperactivity disorder (ADHD) report a greater lifetime history of concussion than those who do not have ADHD –5. For ee age 11 . MoreoveStudies have primarily examined samples of uninjured student–athletes who complete health history surveys at the beginning of sporting seasons. To our knowledge, no prior study has examined the association between ADHD and lifetime history of concussion among youth who present for clinical care after sustaining a concussion. Several studies of concussion specialty clinic patients have reported lifetime history of concussion in their samples. We extracted the proportions from 16 of these studies and determined, on average (weighted mean proportion), about 35% of youth presenting to concussion clinics report having a prior concussion –22, withUsing a large archival dataset, we examined the proportions of youth with and without ADHD who report a lifetime history of concussion when they present for treatment at a Canadian specialty concussion clinic. We hypothesized that (i) youth with ADHD would have a greater lifetime history of concussion than youth without ADHD, and (ii) boys with ADHD would have a greater lifetime history of concussion than girls with ADHD.SD = 2.7, range: 5–18) and equally split between girls (50.9%) and boys (49.1%). The primary  language spoken was French (57.8%), English (41.7%), and bilingual/equal fluency in both languages (0.5%). Data regarding racial and ethnic group affiliation were not available. A trauma coordinator, who is a licensed health care professional, prospectively gathered this information during a customary intake interview using standardized data collection forms, with the parent and child collaboratively providing answers. Specifically, youth and their caregivers were asked whether youth had a pre-injury diagnosis of ADHD (yes or no) and also reported the youth's concussion history (yes/at least one prior concussion or no prior concussion). Those with previous concussions estimated the longest duration of symptoms that they experienced from a prior concussion.Participants included concussed children and adolescents who presented for treatment at a specialty concussion clinic in Montreal, Quebec, Canada consecutively between September 2015 and August 2019. The sample included 2,418 children and adolescents see . The mea2-tests. The proportions of girls and boys with ADHD who experienced a prior concussion were also compared using χ2-tests. To characterize the magnitude of group differences, an odds ratio (OR) was calculated for each analysis as an effect size  (The proportions of youth with and without ADHD who had a history of concussion (one or more prior injuries) were computed and compared using χect size  and inte= large) . Analyse2 = 1.80, p = 0.09, OR = 1.12 . A total of 294 youth (12.2% of the sample) reported having been diagnosed with ADHD. Additionally, sex differences were not observed amongst children and adolescents with ADHD . No overall differences in mechanism of injury or referral source were observed, as 67.4% of youth without ADHD and 63.3% of youth with ADHD were concussed through a sport-related mechanism  and 56.2% of youth without ADHD and 52.7% of youth with ADHD were referred to the concussion clinic from the pediatric emergency room compared to other referral sources .In the total sample, roughly 2 out of 5 (38.3%) concussed youth presenting for care at a specialty concussion clinic had sustained a previous concussion. Boys (39.7%) and girls (37.0%) did not differ with regard to their personal history of concussion, χ2 = 4.41, p = 0.04, OR = 1.30 (1.02–1.67). The proportion with a prior concussion among boys with ADHD [48.1% (40.2–55.9%)] did not significantly differ from girls with ADHD [39.1% (31.0–47.2%)], χ2 = 2.38 p = 0.12, OR = 1.15 (0.91–2.29). However, the proportion with a prior concussion among boys with ADHD [48.1% (31.6–63.1%)] differed significantly from boys without ADHD [38.4% (35.4–41.3%)], χ2 = 5.33, p = 0.02, OR = 1.49 (1.06–2.09). This finding was not observed among girls .A significantly greater proportion of youth with ADHD [43.9% (95% CI 38.2–49.6%)] reported a prior concussion history compared to youth without ADHD [37.5% (35.5–39.6%)], χ2 = 2.56, p = 0.11, OR = 1.28 (0.94–1.75). Of those youth receiving care for a non-sporting injury, the proportion of lifetime history of prior concussion did not differ in youth with ADHD [39.8% (30.6–49.1%)] compared to youth without ADHD [32.2% (28.7–35.7%)], χ2 = 2.46, p = 0.12, OR = 1.39 (0.92–2.12). For individuals referred from the pediatric emergency department, the proportion of prior concussion significantly differed in youth with ADHD [47.7% (39.9–55.6%)] compared to youth without ADHD [38.0% (35.2–40.7%)], χ2 = 5.48, p = 0.02, OR = 1.49 (1.07–2.09). For individuals referred from sources other than a pediatric emergency department, the proportion of prior concussion did not differ in youth with ADHD [39.6% (31.4–47.7%)] compared to youth without ADHD [36.9% (33.8–40.0%)], χ2 = 0.36, p = 0.55, OR = 1.12 (0.78–1.61).We also examined lifetime history of concussion among youth with and without ADHD stratified by the mechanism of their index injury, i.e., their most recent injury, for which they were receiving care (sport-related index injury vs. non-sporting injury), as well as stratified by the referral source for their index injury (emergency room vs. another source). Of those youth presenting for care following a sport-related concussion, the proportion of lifetime history of prior concussion did not differ in youth with ADHD [46.2% (39.1–53.4%)] compared to youth without ADHD [40.1% (37.6–42.7%)], χn = 702), the median longest symptom duration from a previous injury was 21 days . Youth with ADHD  did not differ from youth without ADHD  with regard to longest duration of symptoms from a prior concussion, Z = 1.52, p = 0.13. Further, the proportion of youth who reported taking longer than 28 days to recover from a prior concussion did not differ between those with ADHD (15.3%) and without ADHD (12.2%), χ2 = 2.20, p = 0.14.Among all children and adolescents who reported a prior history of concussion  reporting a prior diagnosis of ADHD compared to about 6% in student athlete studies , 4. Simincussion , and theAs seen in This study is novel in that it is the first to compare the estimated longest symptom duration from previous concussions between youth with and without ADHD. Despite consistent findings that youth with ADHD experience greater lifetime concussion histories, youth with ADHD did not differ from youth without ADHD with regard to longest duration of symptoms from a prior concussion (median for both groups was 21 days). Similarly, the proportion of children and adolescents who reported taking longer than 28 days to recover (commonly considered the demarcation point for “persistent” or prolonged recover) from a prior concussion did not differ between those with ADHD (15.3%) and without ADHD (12.2%). This novel result aligns with conclusions from systematic reviews , 31 as wThis study has several methodological limitations. First, both ADHD status and concussion history were reported by the parents and youth; neither were verified using a clinical evaluation or by reviewing medical records. With that said, caregivers were present with the youth and this health history information was collected during a clinical interview with a trauma coordinator, and thus likely represents a more valid data collection process than many studies on this topic, which rely exclusively on unaided youth self-report of health history. Second, we do not know the subtype or severity of ADHD, age of ADHD onset, current ADHD symptom burden, or whether the youth were on medications for ADHD. Third, we did not have access to racial or ethnic group identification, and more refined examination and understanding of sociocultural factors in concussion research is an important future direction for the field. That said, this study examined Canadian youth whose predominant preferred or dominant language was French, a clinical population that, to this point, has not been characterized with respect to potential associations between pre-existing ADHD and lifetime concussion history. Finally, we do not know how many prior concussions the children and adolescents experienced (the data were coded as present/absent), how long ago the concussion(s) occurred, or the severity of those injuries.Among samples of student athletes and children from the U.S. general population, ADHD is consistently associated with greater lifetime history of concussion. This finding is consistent among the current sample of youth who presented for care at a specialty clinic in Canada, such that among children and adolescents who are seeking health care for an index concussion, those with ADHD report greater lifetime history of concussion than youth without ADHD. However, youth with ADHD did not differ from youth without ADHD in their estimated longest duration of symptoms from a prior concussion—consistent with past studies suggesting that ADHD, per se, does not appear to be a risk factor for prolonged symptoms following concussion.The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. The statistical code, syntax, output, analyses, and minimum dataset are available to qualified researchers upon request.The studies involving human participants were reviewed and approved by Ethics approval was provided by the McGill University Health Centre . Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.NC helped conceptualize the study, conceptualized the statistical analyses, drafted sections of the manuscript, assisted with the literature review, edited the manuscript, and approved the final manuscript. ET helped design and coordinate data collection, conducted the statistical analyses, drafted sections of the manuscript, critically reviewed, and edited/approved the final manuscript. GI helped conceptualize the study, assisted with the literature review, drafted sections of the manuscript, edited the manuscript, and approved the final manuscript. DF and LG critically reviewed and edited/approved the final manuscript. IG helped design and coordinate data collection, wrote the IRB, conceptualized the overall project, critically reviewed, and edited/approved the final manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.® Applications, Inc., National Rugby League, and the Spaulding Research Institute.NC acknowledges support from the Louis V. Gerstner III Research Scholar Award from Massachusetts General Hospital. GI acknowledges unrestricted philanthropic support from the Mooney-Reed Charitable Foundation, Heinz Family Foundation, Boston Bolts, ImPACTGI has a clinical practice in forensic neuropsychology, including expert testimony, involving individuals who have sustained mild TBIs. He has received research support from the Harvard Integrated Program to Protect and Improve the Health of NFLPA Members, and a grant from the National Football League. He serves as a scientific advisor for NanoDx™, Sway Medical, Inc., and Highmark, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.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.


Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder affecting numerous executive functioning skills, such as organizational skills. While the relationship between the inattention aspect of ADHD and poor organizational skills is well documented, it is still unclear if lower organizational skills are only associated with ADHD or if they are also associated with other comorbid disorders commonly diagnosed in conjunction with ADHD. The purpose of the present study is to investigate the relationship between organizational skills and ADHD in adults, as well as the impact of comorbid disorders on ADHD in relation to organizational skills.n = 201 with a diagnosis of ADHD) adults from the general population were recruited online. Participants completed a measure of organizational skills, measures assessing levels of ADHD, depression, and anxiety, and extensive demographic information related to their diagnosis of ADHD or other possible diagnosis.Four hundred seven (Participants with a diagnosis of ADHD were significantly more likely to have a comorbid diagnosis of depression and/or anxiety. Organizational skills were significantly lower in individuals who reported having received a diagnosis of ADHD, but not in those with a past diagnosis of depression and anxiety. However, organizational skills were lower in individuals currently experiencing higher levels of depression and anxiety. The results of regression analyses suggest that levels of organizational skills are most strongly predicted by inattention and more weakly predicted by comorbid disorders.These results highlight the centrality of organizational skills deficit in ADHD as well as the influence of the inattention component over other components commonly observed in ADHD on organizational skills. Suggestions for treatment of ADHD in adults are discussed. The DSM-V lists attention deficit hyperactivity disorder (ADHD) as a neurodevelopmental disorder that causes significant impairments in the areas of attention, hyperactivity, and impulsivity. ADHD can be divided into three subtypes based on severity: inattentive presentation (ADHD-I), hyperactive-impulsive presentation (ADHD-HI), or combined presentation ADHD-C; . WorldwiMany organizational skills are challenging areas for individuals with ADHD which may have long-term impacts if inappropriately addressed. The inattention component may result in a decrease in the ability to plan and organize daily activities, which is a common drawback in some individuals with ADHD in adulthood . Even whOrganizational skills may be particularly relevant in alleviating the negative impact of ADHD, given the link between ADHD and difficulty with executive functioning skills . ResultsExecutive functions seem to be commonly impaired in children with ADHD, even when the symptoms of inattention are not particularly severe . However, some of the most common disorders associated with ADHD include depression and anxiety.The challenges with organizational skills are compounded by the ADHD’s rates of comorbidity. Indeed, over 75% of individuals with ADHD experience at least one other psychiatric disorder . ADHD isInternalizing disorders such as depression and anxiety are notably common among people with ADHD. Depression is alleged to be about five times as common among individuals with ADHD than among those without it . The co-Anxiety is commonplace in around 25% of individuals with ADHD  though tFurthermore, ADHD may have differential impacts depending on the sex of the person affected. ADHD is about 1.6 times more common in men than in women , and it A second factor that needs to be considered is age. In addition to sex and age, other characteristics may moderate the relationship between executive functioning, such as organizational skills, and ADHD. For instance, previous studies suggested that individuals diagnosed with ADHD receiving medication performed better on a series of cognitive tasks as opposed to those with ADHD without medication . Past ste.g., anxiety and depression). We have developed three hypotheses: (1) individuals diagnosed with ADHD will display higher levels of comorbid disorders, such as anxiety and depression; (2) organizational skills will be significantly lower in individuals with a diagnosis or high symptoms of anxiety, depression, or ADHD, with the largest effect size for the ADHD group; and (3) an exploratory regression analysis will examine the influence of comorbid disorders and common characteristics  by diagnosis of ADHD on organizational skills.The relationship between ADHD and other disorders and their combined impact on organizational skills remain unclear. The purpose of this study is to explore the relationship between organizational skills and ADHD, as well as disorders commonly comorbid with ADHD (N = 407) participants were recruited online on Facebook groups dedicated to individuals with ADHD and forums related to psychological research . Inclusion criteria were to be over 18 years old and be fluent in English. There were no exclusion criteria. Participants were 24% male and 76% females. A total of 201 participants (49% of the sample) reported having received an official diagnosis of ADHD from a physician, a psychiatrist, or a psychologist. Participants were mostly native English speakers (81%). Regarding education, participants had either high school or college education (31%), bachelor education (39%), or post-bachelor education (29%). Most participants were in North America (65%) or Europe (25%). Almost half (42%) of the sample reported being currently enrolled as a student in a university. The mean age was 28.40 years old (SD = 9.21). An ANOVA suggests a mean age difference between groups, whereas individuals without an ADHD diagnosis  were younger than those with an ADHD diagnosis   = 13.127, p < .001, d = 0.36). All participants provided written informed consent prior beginning the online study. Participants were not compensated for participating in the study. The present study was approved and given ‘exempt’ status by the IntegReview Ethical Review Board , under protocol number 11022016.A total of four hundred and seven  and on its subscales (α = .60 to .95), as well as adequate construct validity with ADHD symptoms for adults . The scale is divided in two sections: Inattention  and Hyperactivity-Impulsivity . Past research has suggest a cut-off score of 20  to identify individuals with potential ADHD . A higher score reflects higher levels of depressive symptoms. For this study, we used the cut-off score of ≥ 15 to identify individuals with moderately severe to severe depressive symptoms within the last two weeks. The PHQ-9 is commonly used and possesses good psychometric properties of the PHQ-9  supported mean differences between conditions on the DOSQ Total  = 50.638, p < .001), alongside most of its subscales: Work Organization  = 23.474, p < .001), Communication Clarity  = 28.208, p < .001), Punctuality  = 31.946, p < .001), Assiduity  = 103.410, p < .001), and Attentiveness  = 103.464, p < .001). Differences were also observed on both ASRS scales, namely Inattention  = 140.400, p < .001) and Hyperactivity-Impulsivity  = 92.451, p < .001). All analyses were performed by condition (no-ADHD/ADHD). The attribution to one group or the other (no-ADHD/ADHD) was based on the participant’s response to having received (or not) a diagnosis of ADHD from a health professional . Examination of Kurtosis and Skewness support a normal distribution on the DOSQ and its subscales, alongside the ASRS, the PHQ, and the STAI-6. Chi-square tests were performed between groups on six independent variables, namely education, biological sex, depression level, anxiety level, ADHD level, and comorbid diagnoses, and are reported in p = .05/6 = .008 was applied to account for the six analyses. A series of ANOVAs confirmed that lower levels of organizational skills were present in individuals who received a diagnosis of ADHD  = 5.524, p = .019, d = 0.23) or anxiety  = 6.860, p = .009, d = 0.26). Furthermore, lower levels of organizational skills were observed in individuals passing the threshold value on the ASRS  = 201.535, p < .001, d = 1.48) and the PHQ  = 25.053, p < .001, d = 0.52), as well as those above the mean on the STAI-6  = 22.785, p < .001, d = 0.47).Subsequently, we examined the difference in organizational skills in terms of diagnoses and self-reported information related to ADHD, depression, and anxiety. To correct for multiple testing, a Bonferroni correction of F = 38.324, p < .001, 2adjusted R = .36). The only significant predictors were ASRS inattention , ASRS hyper-impulsivity  and self-reported depression total . The first model, containing only inattention, explained 29% of the variance, increasing to 34% when also taking into account self-reported depression and 36% when also including hyperactivity-impulsivity.Lastly, we performed two regression analysis, one per group of ADHD diagnosis, to identify other variables measuring organizational skills. For individuals with a diagnosis of ADHD, a stepwise regression model was computed with the following variables: depression diagnosis, anxiety diagnosis, no diagnosis, sex, age, PHQ total, ASRS inattention total, ASRS hyper-impulsivity total, STAI-6 total, medication, and therapy. The results suggested a significant model  = 88.503, p < .001, 2adjusted R = .68). The significant predictors included sex , anxiety diagnosis , ASRS inattention , ASRS hyper-impulsivity , and self-reported depression total . The first model, containing only inattention, explained 62% of the variance. The variance explained increased to 65% when also including sex, 66% when adding anxiety diagnosis, 67% when including self-reported depressive symptoms, and 68% when adding hyperactivity-impulsivity.A second regression analysis was performed for individuals without a diagnosis of ADHD with the aforementioned predictors, aside of medication and therapy. The results suggested a significant model (p < .001) or anxiety (p < .001). This finding is aligned with the literature, as previous results suggest that individuals with ADHD are five time more likely to have a diagnosis of depression than individuals without ADHD (p < .001).This study examined the relationship between ADHD and comorbid disorders, as well as their relationship with organizational skills. As expected, the results suggest that people with ADHD are more likely to have a comorbid diagnosis of depression  symptoms associated with depression and anxiety. The similar ratio of self-reported anxiety and depression in individuals with and without ADHD could be due to the large proportion of students in the sample size. Multiple studies suggest that university students display high levels of depressive and anxiety-related symptoms . As expeOur findings also suggest that individuals with a diagnosis of ADHD, as well as individuals with higher levels of self-reported depression, anxiety, and stress, display lower levels of organizational skills. Interestingly, this difference was not found for individuals with a current or past diagnosis of depression or anxiety disorder. As previously mentioned, these results may be due to the focus of self-reported instruments on current symptoms. Multiple studies suggest that anxiety and depression negatively impact executive functions concurrently  or yearsIn the present study, two regression analyses were computed to examine the significant predictors of organizational skills. The results suggested that, for both individuals with and without ADHD, the inattention component of the ASRS was the strongest predictor to measure organizational skills, assessing 29% and 62% of the variance for individuals with and without ADHD, respectively. Additional predictors, while statistically significant, were marginal, increasing the total variance by only 7% and 6%, respectively. These results confirm the centrality of the inattention factor in a reduction of organizational skills . It is pThe findings of this study suggest that organizational skills are a core facet of ADHD, weakly related to other comorbid disorders the individual with ADHD may be experiencing. The findings support the study’s hypotheses: individuals with ADHD are more likely to have or have received at some point a diagnosis of anxiety disorder or depression, organizational skills are lower in individuals reporting higher levels of symptoms of ADHD, depression, and anxiety, and regression analyses suggest that inattention is the largest predictor of organizational skills in individuals with and without ADHD.N = 407) which may have prevented finding cases with rare diseases . It was also noted that the current study was possibly too long for those individuals with very severe ADHD. Therefore, it is recommended that future studies be shorter or be delivered in multi-steps. Additionally, the present study did not distinguish between current and past diagnosis of depression and anxiety. While a lack of distinction is less problematic for life-long pathologies (such as bipolar disorder), it is highly possible that some individuals were diagnosed in the past for depression or anxiety but are not currently experiencing symptoms. Future studies should take these cases into account when assessing comorbid disorders. Lastly, the participants included in the sample may not be an adequate representation of the general population. A large proportion of the sample included females with high levels of education. This could be due to some of the recruitment method, which focused on online support groups for ADHD. Future studies should attempt to recruit from the general population while keeping a better ratio between males and females.Future studies should also explore the utility of therapies focused on increasing organizational skills in individuals with ADHD. The development of such interventions is particularly important, given that problems with organizational difficulties are likely to persist even when other ADHD symptoms lessen , and giv10.7717/peerj.12836/supp-1Supplemental Information 1Click here for additional data file.


It is well known that the waveguide beam splitter can be used as a source for the quantum entanglement of photons. The analysis of such quantum entanglement is a difficult problem even for monochromatic photons, since the system under study is multiparametric. This paper will show that quantum entanglement can be represented in a simple form not only for monochromatic photons but also for non-monochromatic ones. It will be shown that quantum entanglement for non-monochromatic photons can be very different from monochromatic photons, which can be used to create large quantum entanglement. It has long been known that the beam splitter (BS) is a source of quantum entangled photons ,2,3,4. SR and transmission T, where R and T are always constant for the waveguide BS. Recently, [R and T depend on the frequencies of the photons fed into both ports of the BS. Taking into account the frequency dependence of the coefficients R and T, many known theories can be modified, for example, the Hong–Ou–Mandel (HOM) theory of interference [R and T is inherent only in the [R and T does not affect the studied effects [R and transmission T. It is also important to present the results in a simple analytical form, without the use of numerical calculations.Theories that describe quantum entanglement of the BS are based on the constancy of its main parameters: reflection coefficient ee e.g., ,17,18,19ecently, ,21 the trference ,23. It sy in the  waveguidR. In the case of non-monochromatic photons, the results are also presented in an analytical form. It is shown that quantum entanglement in the case of nonmonochromatic photons can be of great importance and is very different from the case of monochromatic photons. Quantum entanglement is analyzed using the von Neumann entropy. Unfortunately, the von Neumann entropy is not always convenient for calculating quantum entanglement because it is hard to calculate.In this paper we investigated quantum entanglement of photons on a waveguide BS. For monochromatic photons, the results are presented in a simple analytical form from just one parameter of the system under study, which is the reflection coefficient k is the numbers of photons in the first output ports; p is the numbers of photons in the second output ports; k and R and the transmission coefficient T, which, as shown in [C is proportional to the frequency In the paper  it was s+p=s1+s2 , where |shown in ,22, willonnected . As was hown in [R=sin2ΩtBIn the given Equations –3) you  you 3) yll known ,24,25,26for for for for Let us represent quantum entanglement Let us depict in P for the case RIt should be added that here we give only some values for quantum entanglement depending on the initial states ) states ,27, i.e.or any RPthe formKmax=22s1k and It is well known that the wave function ented as ,21,28,29R, which has a dependence similar to Let us study the quantum entanglement of the system in question. To see how BS leads to the quantum entanglement of photons we will assume that there is no quantum entanglement at the BS input ports. This is a natural assumption, since we need to study how the BS affects the appearance of quantum entanglement. This means that we will consider the incoming Fock states for photons, but the photons are not monochromatic. In this case, as previously well known, the wave function of the photon is factorizable, i.e., ∑kΛklnΛk ,24,25,26Let us show that in the case of non-monochromatic photons, quantum entanglement can be very different from that of the monochromatic photons. It should be added that if we use the result for monochromatic and identical photons , for theexample,  to calcuIn It should be added that one can obtain analytical expressions for the von Neumann entropy at Equation . We do nR and transmission T coefficients are always considered constant (independent of the photon frequencies) when calculating quantum entanglement. Assuming the coefficients R and transmission T depend on the frequencies of input photons in ports 1 and 2 of the BS. One of the main conclusions is the essential difference between quantum entanglement of monochromatic and non-monochromatic photons on the waveguide BS. Another important conclusion is that the quantum entanglement is more significant in the case of non-monochromatic photons. If we consider identical incoming photons, it was obtained that the maximum quantum entanglement at R and T as constant values, our theory in the limiting case is R and T) and non-monochromatic photons.The physical motivation for this work is determined by the fact that it is usually assumed that in beam splitters, regardless of their type, the reflection Equation ) and coiN is the total number of photons in a two-part system, e.g., [R and T, quantum entanglement is a periodic function contingent on R were presented for monochromatic photons. Thus, in this paper the main conclusions and results for the calculation of quantum entanglement on the waveguide beam splitter for monochromatic and non-monochromatic photons and their main differences have been presented.Analyzing the results obtained, we can conclude that the waveguide BS can be a good source of quantum entangled photons. Such quantum entanglement can be easily adjusted by changing the m, e.g., . In our  that in  the simpAs a result, we can say that the waveguide BS can be used as a source of large quantum entanglement of photons and the obtained results can be used in various fields of quantum technologies.


Attention Deficit/Hyperactivity Disorder (ADHD) is the most frequently diagnosed neurodevelopmental disorder in school-age children, and it is usually associated with a significant impairment in global functioning. Traditionally, boys with ADHD are more likely to be referred for clinical assessments due to a higher prevalence of externalizing symptoms. However, as regards gender-related differential clinical characteristics between boys and girls with ADHD, further investigation is warranted in light of conflicting results found in currently available literature. In fact, a more precise clinical characterization could help increase appropriate diagnoses and treatment planning. In this context, we carried out a retrospective observational study on 715 children and adolescents diagnosed with ADHD from 2018 to 2020 at our center, in order to describe their gender-related clinical characteristics. Boys displayed higher average IQs, but they were comparable to girls in functional impairments and adaptive skills. Girls displayed higher scores on the Attention Problems subscale of the CBCL 6–18 and on several CPRS-R:L subscales, suggesting higher general ADHD symptom severity. Boys showed higher scores on CBCL 6–18 subscales, such as withdrawn/depressed, internalizing, and obsessive-compulsive problems. In conclusion, girls showed more severe ADHD features and lower IQ in clinically referred settings, while boys showed more internalizing problems and obsessive-compulsive symptoms. Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in early childhood and adolescence. According to DSM-5, it is characterized by altered and unusual levels of inattention and hyperactivity compared to what is observed in typical child development . ADHD syADHD worldwide prevalence in school-age children is 5.3% . In ItalA representative Danish survey based on health registry data collected from 1995 to 2010 reported that ADHD incidence rates increased by a factor of approximately 12 during this period. Furthermore, it was also reported that the male-to-female ratio decreased from 7.5:1 to 3:1 among school-age children and from 8.1:1 to 1.6:1 among adolescents . These dTraditionally, boys are more likely to be referred, diagnosed, and treated for ADHD symptoms than girls. This seemed to depend on gender differences in symptomatology: for example, males would have more disruptive/externalizing symptoms ,7, whichThis was confirmed by a recent publication, as boys showed higher impulsivity compared to girls while girls displayed higher levels of inattention compared to boys. However, the same study found no differences in the hyperactivity and distractibility levels of boys and girls , suggestAs regards a gender-specific comorbidity pattern in ADHD, the available literature generally supports a higher prevalence rate of externalizing disorders  and symptoms  in boys, and a higher prevalence rate of internalizing disorders  in girls ,10.In this context, our aim is to describe gender-related clinical characteristics of 715 children and adolescents diagnosed with ADHD from 2018 to 2020 in a retrospective observational study. Our results will be discussed in light of the existing international literature, in order to provide additional pieces of evidence, helping to reduce the significant gender-related differences in timely and effectively diagnosing and treating of ADHD in developmental age.In this retrospective study, 715 drug-naïve children and adolescents with ADHD who attended the Child and Adolescents Neuropsychiatry Unit of the Bambino Gesù Children’s Hospital  for a first diagnosis were recruited over the course of 3 years (from January 2018 to December 2020).Children and adolescents (mean age (years) = 9.4, SD = 2.9; 108 girls with ADHD and 607 boys with ADHD) received their first diagnosis of ADHD by experienced developmental psychiatrists and neuropsychologists, according to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria [Only patients without comorbidities were selected from all the cases assessed at our center and finally entered in the final sample of 715 children and adolescents previously described in detail. This choice was made in order to increase homogeneity in results, as the comorbid group was too small and heterogeneous due to the presence of multiple/complex comorbid conditions. Furthermore, patients were included in the study if they met the following criteria: (a) the absence of neurological and neurosensory deficit; (b) the absence of autism spectrum disorder; (c) the absence of past drug treatment; (d) the absence of intellectual disability.Psychiatric diagnoses were based on developmental history, extensive clinical examination, and the Schedule for Affective Disorders and Schizophrenia for School-Age Children—Present and Lifetime Version, DSM-5 , a semi-All participants and parents were informed about assessment instruments and treatment options. Written informed consent was obtained from parents. The study was conformed to the Declaration of Helsinki.Global functioning was assessed with the Children’s Global Assessment Scale (C-GAS) . The C-GIQ was measured by using the Wechsler Intelligence Scale for Children-IV (WISC-IV)  or ColorThe adaptive skills were evaluated by means of The Adaptive Behavior Assessment System—Second Edition (ABAS-II)  or the VConners’ Parent Rating Scales Long Version Revised (CPRS-R:L)  was usedThe Child Behavior Checklist for Ages 6–18 years (CBCL 6–18) [When data were normally distributed and the assumption of homogeneity was not violated, parametric tests were computed. The Shapiro–Wilk test was used to test the normality of the data and Levene’s test for the homogeneity of variances. Student’s t-tests were used to compare the boys vs. girls groups on age, IQ, ABAS-II, and C-GAS. Multivariate analysis of variance (MANOVA) was used to compare the boys and girls groups on CBCL scales and on CPRS-R:L scales. Considering that the two groups differed in IQ, MANCOVA on CBCL scales and on CPRS-R:L scales were conducted, also controlling for IQ. The Fisher LSD test was used for post-hoc analyses on CPRS-R:L subscales, CBCL 6–18 subscales, Group x CPRS-R:L subscales, and on Group x CBCL 6–18 subscale effects. Box’s M test was used to check the equality of multiple variance–covariance matrices.The statistical software SPSS Version 22  was used for analyses. 711 = −0.75, p = 0.45; boys: M = 9.48, SD = 2.89, range = 6–18; girls: M = 9.87, SD = 11.12, range = 6–16), global functioning (C-GAS)  and adaptive skills . However, boys with ADHD displayed higher IQ compared to girls .Boys with ADHD did not differ from girls with ADHD for age in years , while CPRS-R:L subscale effect , and the Group x CPRS-R:L subscale interaction effect  were both significant. Post-hoc analysis (Fisher LSD test) on CPRS-R:L subscales effect demonstrated that all participants showed higher scores in the ADHD index  than all other CPRS-R.L subscales (p < 0.001), with the exception of the DSM IV hyperactive/impulsive subscale . All participants showed higher scores in the DSM IV hyperactive/impulsive subscale  than all other CPRS-R.L subscales (p < 0.001), with the exception of the DSM-IV inattentive subscale .ADHD symptoms (CPRS-R:L) of boys and girls were compared by means of a MANCOVA with 14 CPRS-R:L subscales, within factor and group (boys vs. girls) as between, controlling for IQ (to take into account the difference in IQ between boys and girls with ADHD). The group effect was not significant (Fp > 0.001).Post-hoc analysis (Fisher LSD test) on Group x CPRS-R:L subscale effects showed that girls displayed higher scores than boys in several CPRS-R:L subscales, see 1, 482 = 29.62, p = 0.34). However a significant CBCL 6–18 subscale effect, , and interaction Group x CBCL 6–18 subscale effects  were found. Post-hoc analysis (Fisher LSD test) on the CBCL 6–18 subscale effect documented higher scores in attention problems  than in ADHD problems , and total problems . Post-hoc analysis also showed higher scores in affective problems  than social problems , anxiety problems , thought problems , oppositional–defiant problems , anxious/depressed , conduct problems , internalizing problems , rule-breaking behavior , withdrawn/depressed , obsessive-compulsive problems , sluggish cognitive tempo , somatic complains , and somatic problems . To evaluate differences between boys and girls in behavioral and emotional symptoms (CBCL 6–18), a MANCOVA was conducted with 20 CBCL 6–18 subscales, within factor and group (boys vs. girls), as between factor, controlling for IQ (to take into account the difference in IQ between boys and girls with ADHD). No group effect was found (Fp > 0.001).Post-hoc analysis (Fisher LSD test) on Group x CBCL 6–18 subscale effects showed that boys displayed higher scores in several subscales, see This retrospective observational study investigated gender-related clinical characteristics on a group of 715 children and adolescents at their first diagnostic assessment for ADHD. On the epidemiological level, our results are in line with international literature ,20,21, wOur results showed that boys and girls with ADHD differed for IQ, but they were comparable for functional impairments or adaptive skills. Taking into account the difference in IQ, comparisons between girls and boys on behavioral and psychopathological characteristics showed that girls with ADHD obtained higher scores on the Attention Problems subscale of the CBCL 6–18 and on several CPRS-R:L subscales, suggesting higher general ADHD symptom severity. However, boys showed higher scores on CBCL 6–18 subscales, such as withdrawn/depressed, internalizing, obsessive-compulsive problems, related to mood and internalizing problems.Previous findings have suggested that clinically diagnosed males and females usually showed similar symptom severity, except for higher inattention in females ,23,24. OThis could be at least partially explained by a referral bias, as it is possible that only the most severe girls were referred for early assessment and diagnosis at our ward, because predominantly inattentive aspects were generally harder to detect and less disturbing in the classroom or at home . MoreoveHowever, recent findings by Slobodin and Davidovitch  are in lAs already mentioned, in our sample, boys with ADHD displayed a slightly higher IQ than girls. In turn, girls showed more pronounced ADHD features. However, this did not reflect higher functional impairments or deficits in adaptive skills in girls as compared to boys. With respect to this, on the one hand, the difference in IQ could be accounted for by the fact that girls are more frequently assessed, diagnosed, and treated either when they present with extremely severe ADHD global symptoms or when they suffer from prominently impaired inattention . On the Therefore, a picture emerged where the most significant differences between boys and girls with ADHD, within the clinically referred population, were quintessentially on behavioral and emotional symptoms. Among these differential symptomatological features, we found significantly higher levels of obsessive-compulsive symptoms in boys than in girls. The obsessive-compulsive dimension is usually characterized by intrusive thoughts and the need for compulsion. Further, it is usually associated with an inhibited temperament that is generally characterized by behavioral restraint, withdrawal, and avoidance of novel stimuli ,33. At tFinally, ADHD and obsessive-compulsive dimensions share some common neurobiological features, including dysfunctions in cortical–striatal–pallidal–thalamic circuits , and areTraditionally, evidence emerging from studies on gender-related clinical characteristics in the normal population is usually consistent with a higher prevalence of internalizing symptoms, such as behavioral inhibition, worry, and anxiety symptoms, in girls ,39. ThisThe main limitation of our study is that we did not assess characteristics in the general population that were not clinically referred, where ADHD seems to be more represented in females than it usually is in clinical samples . For thiAnother potential limitation is that consistent and reliable information on ADHD diagnosis of the parents or other clinically significant data regarding parents were not available in our dataset. This is also because ADHD is rarely diagnosed in adults in Italy, and it was rarely diagnosed in developmental age when the parents of our patients were children. Therefore, it is possible that at least part of the parents have undiagnosed persistent adult ADHD.We acknowledge that our paper reports on a much-studied topic in ADHD; for this reason, its originality for readers may be considered limited. Nonetheless, we reckon that it provides a very large sample of drug-naïve children and adolescents with ADHD at their first evaluation/diagnosis. Furthermore, our results highlight the fact that internalizing symptoms in a clinically referred sample may be more pronounced in boys, which is, in our opinion, something that should not be overlooked by clinicians in order to properly tailor multimodal treatment strategies to patients.As for the potential differential impact of pharmacological and non-pharmacological treatments on gender-based clinical characteristics, follow-up studies on the same sample are ongoing. These studies will also help understand the potential gender-related impacts of treatment after 3 and 6 months of methylphenidate in the most severe cases needing pharmacological treatment. Finally, a distinct paper on age dependence of psychopathological characteristics of children and adolescents with ADHD is in preparation. In that context, the hypothesis that the aforementioned characteristics could differ by age within each gender group will be tested.In conclusion, our study provided evidence that girls showed more severe ADHD features and lower IQ in a clinically referred setting, while boys showed more prominent internalizing problems and obsessive-compulsive symptoms.Our results are important for clinicians, to consider evaluating and treating young children with ADHD. Indeed, girls with ADHD who are clinically referred for evaluation may display significantly severe presentations, particularly inattention, in which pharmacological treatment with methylphenidate is often warranted. On the other hand, internalizing symptoms should not be overlooked in boys with ADHD, as they could be specifically targeted by cognitive–behavioral treatment along with ADHD symptoms. Further studies with longitudinal designs are needed in order to establish whether (and how) pharmacological and non-pharmacological treatments for ADHD could positively impact gender-based differential clinical characteristics in the modeling of the developmental trajectory of the disorder.


Moreover, morning cortisol levels were lower in ADHD youth when compared with TD youth , while there is no difference for the afternoon cortisol levels (p = 0.48). The meta-analysis on inflammation biomarker was conducted on 4 studies  showed that Tumour Necrosis Factor-alpha (TNF-α) was lower in ADHD when compared with TD , while no differences for Interleukin-1β(IL-1β) (p = 0.21), IL-6 (p = 0.09) and IL-10 (p = 0.77). The lower cortisol in the context of low TNF-α levels may indicate a specific pattern of biomarkers in ADHD, and further investigation is warranted.Several studies reported abnormal cortisol and inflammatory biomarker levels in youths with attention deficit hyperactivity disorder (ADHD), but the results have not been conclusive. We conducted a systematic review followed by a meta-analysis of case-control studies assessing blood or saliva cortisol levels and blood levels of inflammatory biomarkers in youth with ADHD. The effect sizes (ES) were synthesized by using a random-effects model. In the 19 studies on cortisol levels (totaling  Hypothalamus-Pituitary-Adrenal (HPA) axis dysfunction and inflammation have been suggested to contribute to the development of attention deficit hyperactivity disorder (ADHD). Steingard et al. were the first to describe abnormalities of the HPA axis in children with ADHD, as they described a higher rate of dexamethasone non suppression in children with ADHD when compared with controls 22.7% vs. 0%) . Subsequ% vs. 0% Inflammation has also been suggested to play a crucial role in the pathogenesis of ADHD. Epidemiology, genetic studies, and a recent systematic review have provided support by showing high comorbidity of ADHD with inflammatory and autoimmune disorders and the differences in biomarkers between ADHD and TD youth –13. For Furthermore, ADHD has been suggested as the result of an exaggerated central nervous system (CNS) inflammatory response in fetus associated with maternal inflammation . NeonataIn this study, we have updated the previously published meta-analysis  of cortiWe conducted a systematic review and a meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines .To identify eligible studies in the systematic review and the meta-analysis, a computerized search was performed for studies available as of 19th of January 2021 in the electronic databases of PubMed at the National Library of Medicine. The search for cortisol levels was performed by using the search terms: (attention deficit hyperactivity disorder OR ADHD) AND cortisol, without special limitation in language. References lists from identified articles and relevant reviews were scrutinized for studies not indexed in the electronic databases. The search for inflammatory biomarkers was performed by using the search terms: (attention deficit hyperactivity disorder OR ADHD) AND inflammation; (attention deficit hyperactivity disorder OR ADHD) AND (c-reactive protein or CRP); (attention deficit hyperactivity disorder OR ADHD) AND interleukin; (attention deficit hyperactivity disorder OR ADHD) AND (tumour necrosis factor-α or TNF-α), without special limitation in language. References lists from identified articles and relevant reviews were scrutinized for studies not indexed in the electronic databases. Our initial search identified 154 studies for the cortisol levels  measured levels of cortisol of blood or salivary sample; (2) participants were school-aged children (4–12 years) and adolescents 13–17 years) who had a diagnosis of ADHD; (3) the data allowed to calculate an effect size; and (4) the publications were in peer-reviewed journals. Nineteen studies –17 years, 28–37 wOur criteria were: the studies (1) measured levels of inflammatory biomarkers including C-reactive protein, interleukin (IL)−1β, IL-6, IL-10, and tumour necrosis factor (TNF-)α; (2) participants were school-aged children (4–12 years) and adolescents (13–17 years) who had a diagnosis of ADHD; 3) the data allowed to calculate an effect size; and (4) the publications were in peer-reviewed journals. Four studies , 38, 39 Studies that included and re-analysed the same data set as previously published studies were not regarded as independent, and in this case only the study with the highest number of participants was included. See Supplementary Fig. ES) expressing the difference in the cortisol levels ((or the area-under-the-curve with respect to the ground (AUCg) if diurnal cortisol was measured in the study) or differences in the inflammatory biomarker levels between ADHD and controls, were described as the standardized mean difference (SMD) on the basis of Hedge’s adjusted g, in which a value lesser than 0 indicated that cortisol levels or inflammatory biomarker levels were higher in ADHD subjects. When these data could not be retrieved from the publications, we contacted the authors to acquire the data of derived ES from other measures of variability. The results of individual studies were synthesized by the random-effects model [I2 statistic assessed heterogeneity between studies. Publication bias was assessed using the Egger regression asymmetry tests (and inspection of the regression asymmetry plot) and the Begg adjusted rank correlation test. There was no publication bias in the 19 studies . Meta-analyses were conducted by applying STATA [p values < 0.05 were considered statistically significant.In our analysis, the primary outcomes were comparisons of (1) cortisol levels and (2) levels of IL-1β, IL-6, IL-10, and TN-α, between ADHD and controls. For each identified study, the effect size      in children with ADHD and TD children showed that ADHD youth have lower basal cortisol levels, independently from the time-point   and 6 studies measuring awakening salivary cortical levels  confirmed that youth with ADHD have lower salivary cortisol levels than TD youth   showed no differences between youth with ADHD and TD youth , and found again that youth with ADHD have lower salivary cortisol levels than TD youth   or IL-10 (p = 0.77) levels between children with ADHD and TD children   . Howeverg = 0.27; p = 0.010; g = 0.24; p = 0.020, respectively). The heterogeneity (I2) also decreased . These two studies may differ from the rest of the included studies in that they included participants much younger than the rest of the other studies (most of the studies included participants older than 6 years of age), where the participants in Maldonado et al.’s study range from 5–8 years of age and in McCarthy et al.’s study range from 4–10 years of age. Moreover, the inclusion criteria for McCarthy’s study are parental reports of the ADHD diagnosis and the use of stimulants, rather than diagnosis made by clinical professionals or by DSM structured interviews.Although the sensitivity analysis showed that the main results of the meta-analysis did not change after the removal of any one of the included studies. Maldonado et al. 2009  and McCaOf note, it has been reported that the immunoassays measuring cortisol do not just measure cortisol, but also measure inactive 5α-reduced metabolites of cortisol , 55. Thep = 0.0001) [n = 117) showed no difference between salivary cortisol levels of ADHD and normal controls (p = 0.13), but there was a numerical trend showing adults with ADHD having lower salivary cortisol levels [Our study finding is overall consistent with Scassellati’s meta-analysis of 8 studies of salivary cortisol levels, where patients with ADHD had lower cortisol levels when compared to TD subjects ( 0.0001) . We have 0.0001) , 23, 26, 0.0001) , 31, 36,Another interesting finding of our meta-analysis is that children with ADHD have a lower level of TNF-α than control children. This finding is different from previous study findings showing no difference in TNF-α levels in ADHD subjects, both youth and adults, and normal controls , 19. On IL-6 gene in children with ADHD; moreover, the A and G alleles of the 174 polymorphism have been associated with measures of attention, with people with the IL-6 AA homozygotes performing better than IL-6 homozygotes [Although there is no difference in the interleukin levels between ADHD and TD youth in our meta-analysis, there is a trend showing that IL-6 is higher in ADHD youth than in TD youth. The finding may be not significant due to a lack of power, as genetic, animal, and clinical studies have suggested a potential role of IL-6 in ADHD pathogenesis. For example, one study showed an increased frequency of the C allele at the 174 polymorphism of the ozygotes . Animal ozygotes . Moreoveozygotes . In addiozygotes , althougozygotes . Finallyozygotes . Thus, mozygotes . The incozygotes , and thaozygotes .A number of biological mechanisms have been proposed to explain the HPA axis and inflammatory abnormalities in ADHD. Youth with ADHD may have an under-active behavioural inhibition system (BIS) , and an The main limitation of our meta-analysis is that we did not control for confounders such as diet and exercise, and that we did not subgroup the subjects into the three ADHD subtypes , as these data were not consistently present in the included papers. Moreover, we did not account for the cortisol awakening response, therefore we cannot draw any conclusion on the stress reactivity in ADHD. However, a previous meta-analysis on cortisol reactivity in response to a stressor in ADHD showed no difference between cortisol reactivity and ADHD, but implied that there is significant heterogeneity in the analyses that might serve as moderators of this association . In addiWe find, in our meta-analysis of existing studies, that children with ADHD have lower basal cortisol levels, especially morning cortisol levels, than TD youth. The lower cortisol levels in ADHD further imply the role of HPA axis hypoactivity in ADHD pathogenesis. In addition, since salivary cortisol levels are often normalized after the child receives treatments with stimulant medications; further attention is warranted for the development of potential treatment strategies involving the normalization of salivary cortisol levels in youth with ADHD. Moreover, we also found that ADHD youth have lower TNF-α levels than TD youth, thus suggesting future studies should investigate TNF-α as a potential biomarker in ADHD.Figure S1Figure S2Figure S3Figure S4Figure S5Table S1Table S2Table S3


Attention deficit hyperactivity disorder (ADHD) is one of the most widespread mental disorders and often persists from childhood to adulthood, and its symptoms vary with age. In this study, we aim to determine the disrupted dynamic functional network connectivity differences in adult, adolescent, and child ADHD using resting-state functional magnetic resonance imaging (rs-fMRI) data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years). We hypothesized that functional connectivity is time-varying and that there are within- and between-network connectivity differences among the three age groups. Nine functional networks were identified using group ICA, and three FC-states were recognized based on their dynamic functional network connectivity (dFNC) pattern. Fraction of time, mean dwell time, transition probability, degree-in, and degree-out were calculated to measure the state dynamics. Higher-order networks including the DMN, SN, and FPN, and lower-order networks comprising the SMN, VN, SC, and AUD were frequently distributed across all states and were found to show connectivity differences among the three age groups. Our findings imply abnormal dynamic interactions and dysconnectivity associated with different ADHD, and these abnormalities differ between the three ADHD age groups. Given the dFNC differences between the three groups in the current study, our work further provides new insights into the mechanism subserved by age difference in the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group. Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders worldwide, characterized by inattentive, hyperactive, or impulsive behaviors . ADHD moFunctional magnetic resonance imaging (fMRI) has become a popular technique for studying brain diseases or disorders such as ADHD. The majority of these ADHD fMRI studies have followed a task-based approach, aiming to examine how brain function may be modulated by group status during cognitive task performance. This approach is designed to isolate specific cognitive processes that may be linked to or modified by ADHD symptoms or treatment. In recent times, however, there has been an overwhelming interest in an alternative method called resting-state functional magnetic resonance imaging (rs-fMRI). The term “resting state” is misleading because the brain is never at rest . It is oUnder resting-state conditions, intrinsic networks obtained from rs-fMRI correlate with low frequency BOLD signal fluctuations between regions of the brain . It has Having proven that FC of the resting brain is indeed dynamic , methodsIn recent times, some researchers have observed time-varying connectivity patterns among intrinsic networks in mental disorders, such as schizophrenia and bipolar disorder, that cannot be detected using sFNC . DynamicEven though dFNC has been employed to study the differences between healthy control subjects and patients in several mental disorders including ADHD, no study has employed dFNC to access network connectivity differences between child, adolescent, and adult ADHD patients. Given that ADHD symptoms vary among the three age groups, identifying network disruptions specific to each age group will provide new insights into the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group. We, therefore, performed dFNC using group independent component analysis (GICA), sliding window correlation, and K-means clustering to explore network connectivity differences in ADHD between these three age groups. We hypothesized that dFNC can capture the time-varying characteristics of fMRI data perculiar to the three ADHD age groups.Unprocessed resting-state fMRI data of ADHD patients (158 subjects) were obtained from the New York University Child Study Center for the ADHD-200 Global Competition and UCLA dataset . The NYU∗-weighted echo-planar imaging (EPI) sequence with the following parameters: slice thickness = 4 mm, slices = 34, TR = 2 s, TE = 30 ms, flip angle = 90°, matrix 64 × 64, FOV = 192 mm. Additionally, a T2-weighted matched-bandwidth high-resolution anatomical scan (with the same slice prescription as the fMRI scan) and MPRAGE were collected. The parameters for the high-resolution scan were: 4 mm slices, TR/TE = 5,000/34 ms, 4 averages, matrix = 128 × 128, 90-degree flip angle. The parameters for MPRAGE were the following: TR = 1.9 s, TE = 2.26 ms, FOV = 250 mm, matrix = 256 × 256, sagittal plane, slice thickness = 1 mm, 176 slices. For the child and adolescent datasets, MRI data were obtained using Siemens Magnetom Allegra Syngo Mr 2004a. FMRI data were collected using an echo-planar imaging sequence with the following parameters: slice thickness: 4 mm, Slices: 33, TR: 2,000 ms, TE: 15 ms, Rotation = 90°, FoV phase: 80.0%, FoV read = 240 mm. In addition, T1-weighted images were acquired using the following parameters: Slice thickness = 1.33 ms, TR = 2,530,ms, TE = 3.25,ms, rotation = 0 degrees, FoV phase = 100.0%, FoV read = 256 mm.For the adult dataset, MRI data were acquired on one of two 3T Siemens Trio scanners, located at the Ahmanson-Lovelace Brain Mapping Center (Siemens version syngo MR B15) and the Staglin Center for Cognitive Neuroscience (Siemens version syngo MR B17) at UCLA. Functional MRI data were collected using a T2For the adult dataset, the first 2 time points were removed, leaving final time points of 150. For the pediatric (adolescent and child) datasets, the first 26 time points were removed to ensure that all the data have equal time points since the time courses are used in the dFNC calculations. The same preprocessing steps were done for all subjects, including slice time correction, realignment, co-registration of T1 images to corresponding functional images, segmentation, normalization by Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL) , and res1 A relatively high model order with 60 components was performed using the Infomax algorithm with a best-run selection from 10 randomly initialized analyses to achieve a functional parcellation of refined cortical and subcortical components corresponding to known anatomical and functional segmentations  to remove any remaining sources of noise. DFNC was estimated based on the sliding window approach. Based on previous studies , we seleK-means clustering was used to cluster all dFNC windows based on the correlation distance. Clustering numbers from 2 to 10 were selected, representing cluster states. For each k, the clustering algorithm was repeated 500 times to increase the chances of escaping local minima, with random initialization used to obtain the state cluster centroids. The optimal number of clusters was estimated using the elbow criterion and silhouette algorithms. An optimal K = 3 was obtained using these two methods. Also, subjects were well distributed among these three clusters, which is better for pattern evaluation. Only the selected 50 ICNs were used in the clustering, resulting in a total of [50 × (50–1)]/2 = 1,225 features. These features were then used for the dynamic FNC analysis between the three groups.Functional network connectivity, fraction of time, mean dwell time, transition probability, degree-in, and degree-out were compared between the three age groups in each state. Correlation analyses were also performed to determine the impact of age, overall ADHD severity, hyperactivity severity, and inattentive severity on fraction of time and mean dwell time of each age group in each cluster state. All statistical analyses were performed using MATLAB . Age, gender, and mean framewise displacement (mean FD) were used as covariates for statistical analyses. Furthermore, given that our data was obtained from different sites, and several studies have shown the effect of multi-site in different ADHD age groups , we regrSpatial maps of the ICNs and their time courses were decomposed using GICA. The selected 50 ICNs were further categorized into nine networks based on their anatomical and functional properties, including the sensorimotor network (SMN), visual network (VN), default-mode network (DMN), central executive network (CEN), cerebellum network (CBN), subcortical network (SC), auditory network (AUD), frontoparietal network (FPN), and salience network (SN). The identified ICNs with their activation peaks primarily fell on the gray matter .Three reoccurring dFNC states over time were identified using K-means clustering and the cluster centroid of each dFNC state is shown in t-tests were done between 1. Adolescents vs. children 2. Adults vs. children 3. Adults vs. adolescents.To find out if there are significant dFNC differences between the three age groups, two-sample p < 0.01, FDR corrected) between adolescent and child ADHD groups. Relative to child ADHD patients, adolescent ADHD patients showed increased network connectivity between the DMN and CEN, DMN and SC, and DMN and SN in state 1. Compared to Child ADHD patients, adolescent ADHD patients exhibited increased network connectivity between the SMN and SC in state 2.t-test between adults and children are shown in p < 0.01, FDR corrected). Relative to child ADHD patients, adult ADHD patients showed increased network connectivity between the SMN and VN, SMN and DMN, SMN and SC, and between the AUD and SC, AUD and DMN, AUD and SN, AUD and FPN, and FPN and CBN in state 1. In state 2, the connectivity pattern changed, with increased network connectivity between the DMN and SMN, DMN and VN, DMN and CBN, DMN and AUD, DMN and FPN, and DMN and SN in adult ADHD patients relative to child ADHD patients. The connectivity pattern again changed in state 3 with more network connectivity differences between the two groups. The DMN showed increased connectivity with the CEN, SC, AUD, FPN, and SN, whiles the VN showed decreased connectivity with the SC in adult ADHD patients relative to child ADHD patients. Compared to the child group, increased connectivity was found within the SMN and AUD, and between the SMN and AUD, SMN and SN, AUD and CBN, AUD and FPN, and AUD and SN in the adult group in state 3.The results of the two-sample p < 0.01, FDR corrected) (All three cluster states showed significant differences between the 2 groups (rrected) . In statt-tests . There was no significant fraction of time difference between adolescent and child ADHD pair in any of the three cluster states.The proportion of time each subject stayed in each state within the whole scan duration was defined as the fraction of time in that state. The average transition matrices and transition probabilities of each age group are shown in t-tests were used to determine the differences between the three groups. Significant degree-in differences were found between adult and adolescent, and adult and child pairs in state 1 , whiles state 2 shows significant differences between only adult and child pair    and the rrected) . Howeverrrected) . No signCorrelation analyses were performed to determine the impact of age, overall ADHD severity, hyperactivity severity, and inattentive severity on fraction of time and mean dwell time for each age group in each cluster state using gender and age as covariates. No significant correlations were found between fractions of time and mean dwell time for the above-mentioned measures in all three states in adult ADHD patients. A significant positive correlation was found between overall disease severity and fraction of time in state 2 in the child group . In the In this study, we investigated time-varying network connectivity patterns and network disruptions in child, adolescent, and adult ADHD patients using ICA, sliding windows, and K-means clustering. Our analysis revealed the following results: (1) unique state network alterations between the resting-state networks were found in all the three groups of ADHD patients with disruption occurring mainly in lower-order functional networks including SMN, AUD, VN, and CBN, while higher-order networks  showed rather sparse and low connectivity; (2) changes in state vectors as a measure of dynamic changes were obtained for the three groups including mean dwell time, fraction of time spent, number of transitions across states, and total transition measure by the degree in and out of state; (3) mean dwell time was positively correlated with overall disease severity and hyperactivity severity in only the adolescent group, whiles fraction of time was positively correlated with overall severity in the child group and hyperactivity severity in the adolescent group.To the best of our knowledge, our work is the first to explore dFNC of the three ADHD age groups, adding to the increasing literature on the evidence of dFNC  and how Three reoccurring states were found using K means clustering, and the connectivity patterns were relatively similar across the groups in all three states . We obseOn the group comparing of the k-mean clusters and the group level connectivity difference, we found different state patterns peculiar to each group. In our state vector analysis, several temporal measures were captured across all states for each group. Firstly, both the dwell time and fraction of time showed significance between each pair of groups in states 1 and 2 except state 3 Although we showed both temporal and spatial dFNC among the three groups, the result should be interpreted carefully. Firstly, the sample size was a limitation in this study, as connectivity tend to be more stable with an increasing number of participants, we were not able to investigate whether our relatively small sample influence the result presented, we suggest that future studies use larger sample sizes to determine the dFNC differences between the three ADHD age groups. Secondly, albeit the control of sex variable in the individual group analysis, the male to female ratio in the adult dataset was not the same as relative to child and adolescent datasets, which may interfere with brain state, future studies may consider recruiting more samples to include balanced sex ratio.In sum, this study investigated the temporal and spatial dynamics of ADHD patients. Nine networks were identified using group ICA. Higher-order networks including the DMN and FPN and lower-order networks comprising the SMN, VN, and AUD were frequently distributed across all states and were connected within and between networks. We also found significant differences in measures such as mean dwell time, fraction of time, degree-in, and degree-out among the three age groups. Generally, all groups did not make full significant temporal transitions, only states 1 and 2 exhibited dynamic variability among the three groups. Our findings imply abnormal dynamic interactions and disconnectivity associated with ADHD. However, these abnormalities differ between the three ADHD age groups, especially when compared between child/adolescent and adults. Overall, the current work highlighted the dynamic properties of the brain captured through sliding window correlations. Furthermore, given the dFNC differences among the three groups, our work provides new insights into the mechanism subserved by age difference in the pathophysiology of ADHD and may set the grounds for future case-control studies in the individual age groups, as well as serving as a guide in the development of treatment strategies to target these specific networks in each age group.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.EA, BK-B, and BB designed the study. EA organized the data and performed the analysis. EA, BK-B, and PW performed the statistical analysis. EA, BK-B, BB, HY, and SV reviewed the results. EA, BK-B, HY, and BN worked on the figures. EA and BK-B wrote the manuscript. All authors read, contributed to the revision of the manuscript, 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.