With the popularity of computers, the internet, and the global spread of COVID-19, more and more attention deficit hyperactivity disorder (ADHD) patients need timely interventions through the internet. At present, there are many online intervention schemes may help these patients. It is necessary to integrate data to analyze their effectiveness.Our purpose is to integrate the ADHD online interventions trials, study its treatment effect and analyze its feasibility, and provide reference information for doctors in other institutions to formulate better treatment plans.We searched PubMed, EMBASE and Cochrane libraries. We didn't limit the start date and end date of search results. Our last search was on December 1, 2021. The keyword is ADHD online therapy. We used the Cochrane bias risk tool to assess the quality of included studies, used the standardized mean difference (SMD) as an effect scale indicator to measure data. Random effects model, subgroup analysis were used to analyze the data.Six randomized controlled trials (RCTs) were identified, including 261 patients with ADHD. These studies showed that online interventions was more effective than waiting list in improving attention deficit and social function of adults and children with ADHD. The attention deficit scores of subjects were calculated in six studies. The sample size of the test group was 123, the sample size of the control group was 133, and the combined SMD was −0.73 . The social function scores of subjects were calculated in six studies. The sample size of the experimental group was 123 and the control group was 133. The combined SMD was −0.59 .The results show that online interventions of ADHD may be an effective intervention. In the future, we need more online intervention researches to improve the symptoms of different patients, especially for some patients who have difficulties in accepting face-to-face treatment. Attention deficit hyperactivity disorder (ADHD) is currently considered to be a developmental disorder. It occurs in childhood and is characterized by difficulty in concentration, hyperactivity, and impulsive personality. The pathogenesis of ADHD has not been thoroughly studied, and various studies show the complexity of the disease  working independently considered the potential eligibility of each of the abstracts generated by the search strategy. We will ask the authors of some studies for help by email so that we can obtain all the data. Articles are screened independently by the three reviewers and any differences will be settled through negotiation.Our study only included RCT, and the intervention form of the experimental group must be online intervention. The intervention form of the control group should be waiting list. The results were evaluated within 1 month after treatment. The subjects were patients with ADHD. The evaluation tool is the relevant scale used by ADHD. Considering that there are many scales to evaluate attention deficit and social function, we choose validated scales such as ADHD-Rating Scale (ADHD-RS)  The name of the first author; (2) Year of publication of the study; (3) Sample size of the study; (4) The mean and standard deviation (SD) of the subject's age or the subject's age range; (5) Outcome indicators; (6) Research intervention methods; (7) Duration of intervention. The above information is summarized in We used Review Manager 5.3 to analyze the data of each study. The efficacy was evaluated according to the mean and SD of each study , implementation (including blind method of researchers and subjects), measurement , follow-up (integrity of results data), report (selective report of research results), and others (other sources of error). The judgment results of “low-risk bias,” “high-risk bias,” and “unclear” were made according to the bias risk assessment criteria. It is displayed in different colors  , 2.Z or U value or chi-square value, the probability p-value under this statistic is obtained, and the significance level is set to 0.05. If P < 0.05, the pooled effect of these studies is statistically significant. An I-squared value of 25% is considered as low, 50% as moderate, and 75% as high heterogeneity  as the effect scale index of the measurement data . The heterogeneity test I2 was 17%, (P = 0.30). In addition, we extracted relevant data from six studies and analyzed the subjects' social function related scores (P < 0.00001). The heterogeneity test I2 was 5%, (P = 0.38). Both of them belong to low heterogeneity.We analyzed ADHD scores in six studies using a random effects model , with a d scores . The comI2 = 0% (P = 0.48), attention score for children and adults, I2 = 0 (P = 0.64), social function score for patients and educators, I2 = 0 (P = 0.83), and social function score for children and adults I2 = 65.8% (P = 0.09). There was no heterogeneity in these four subgroup analyses (Considering the differences between adults and minors in age, psychology, physical development and other functions, we analyzed the subjects of different ages in groups. Considering that the intervention to the patients themselves in the network intervention belongs to direct intervention and the intervention to the patients' guardians belongs to indirect intervention, we conducted subgroup analysis on different intervention objectives. According to the intervention method, patient age, and test results, we conducted four subgroup analyses, and the scores of relevant scales were used as the outcome indicators. We analyzed four subgroups: attention score for patients and educators, analyses –7.P = 0.851. According to the egger's test of the score of social function, P = 0.483. There was no publication bias in the study.Egger's test in Stata 14 were used to analyze the publication bias of the study, and we drew the funnel plot , 4. AccoWe searched a total of six studies that met the criteria and conducted a meta-analysis of six studies. We found that the response of ADHD patients after online intervention is better than that of the control group. This treatment can improve their attention and improve their social function, which may be a potential treatment.At present, although some medical organizations in the United States Pliszka, , Canada In addition to the test results, another objective of our study is to study the characteristics of effective interventions. In 3 of the 6 trials, educators for patients with ADHD showed the importance of the intervention environment. Since some of the participants included in the study are in school-age or even preschool stage, it is reasonable to have the influence of parents and teachers among the factors of intervention. Children of these ages are more likely to need some form of educator's help, including long-distance treatment and face-to-face intervention in life. A study has long shown that if children live in a conflict environment or abnormal parenting environment, it may lead to the symptoms of ADHD, and online intervention is more convenient to help children's families correct education and life problems.The convenience of intervention implementation is the prominent advantage of online intervention. Before 2017, the number of global internet users had reached 3.5 billion. In front of users of this order of magnitude, even evidence-based medicine programs with low completion rates can provide considerable potential health impact (Rogers et al., In addition to discussing the characteristics of each trial separately, we can try to integrate various online intervention to form a new comprehensive intervention. The targets of these interventions include the patient's parents, grandparents, and other guardians. Doctors can use the influence of family environment on ADHD children's attention and social function to reduce their negative emotions, improve their ability to pay attention to things in the process of growth, reduce the risk of children's future attention reduction, and provide a rehabilitation environment for attention deficit children. At the same time, doctors can help children's families create a good family atmosphere, let children learn appropriate skills and communication methods with their families in the process of daily communication, improve bad behavior problems, and make patients have enough physical and mental adaptability to adapt to social communication. At the same time, this kind of education may be aimed at educators such as teachers in the school or institution where the patient is located to create an appropriate learning atmosphere, which is not only effective for ADHD patients but also helpful for some patients having symptoms similar to ADHD, to help some patients with implicit attention deficit. The improvement of this way of education may not only help patients improve their attention problems, but also help patients improve their social skills, gradually adapt to the new social ways in the real society, improve and correct their related functions, improve people's tolerance and understanding of patients, and improve the humanistic quality of the whole society. In the intervention of the target population, we should not only carry out symptom trainings regularly but also formulate portable and feasible training procedures for the target population to help them train in their spare time at any time, improve their training initiative and promote their cooperation. In addition, we can also carry out psychological education for patients to help them understand the benefits of improving their ability, or the psychological factors affecting their attention, and help patients focus on improving their ability instead of focusing on various entertainment activities to consume their limited attention.At present, genetic studies show that the pathogenesis of ADHD can not be explained by genetics (Posner et al., Our study has some limitations. In terms of evidence validity, due to the limited number of studies retrieved and the relatively small sample size, it may not be easy to fully explain the specific effect of ADHD online treatment and will affect the credibility of the results. In addition, although statistical studies do not show significant heterogeneity, there may also be heterogeneous, which is caused by factors such as race, gender, cultural background, and different statistical scales. The differences between some subjective factors and the scale may also affect the results, so these factors may lead to bias. Therefore, more researches are needed on the online treatment of ADHD. However, we believe that the development of online intervention is not without cost. In the research process, we rarely see statistical data describing the research and development cost of these studies. Doctors should comprehensively consider this factor to promote the popularization of online treatment technology.Through this study, we can see that the effects and methods of network treatment of ADHD are slightly different, but in general, it may be an effective treatment and has a lot of room for development. ADHD has its unique characteristics. It is a chronic disease, and the course of the disease can last until adulthood. Therefore, whether the treatment effect can be maintained at a certain level and whether there is corresponding continuous nursing is very important. The current research on the development of treatment is not enough, and the research on network interveners is not enough. At the same time, doctors may strengthen the case study of ADHD to help us better understand its internal mechanism.The original contributions presented in the study are included in the article/SS: conception and design of the study. SS and YL: manuscript preparation and study conceptualization. SX: study design. NZ and JY: final revision and grammar editing. JD and JW: statistical analysis. 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.


Treatment of ASD is notoriously difficult and might benefit from identification of underlying mechanisms that overlap with those disturbed in other developmental disorders, for which treatment options are more obvious. One example of the latter is attention-deficit hyperactivity disorder (ADHD), given the efficacy of especially stimulants in treatment of ADHD. Deficiencies in catecholaminergic systems  in ADHD are obvious targets for stimulant treatment. Recent findings suggest that dysfunction in catecholaminergic systems may also be a factor in at least a subgroup of ASD. In this review we scrutinize the evidence for catecholaminergic mechanisms underlying ASD symptoms, and also include in this analysis a third classic ascending arousing system, the acetylcholinergic (ACh) network. We complement this with a comprehensive review of DA-, NE-, and ACh-targeted interventions in ASD, and an exploratory search for potential treatment-response predictors (biomarkers) in ASD, genetically or otherwise. Based on this review and analysis we propose that (1) stimulant treatment may be a viable option for an ASD subcategory, possibly defined by genetic subtyping; (2) cerebellar dysfunction is pronounced for a relatively small ADHD subgroup but much more common in ASD and in both cases may point toward NE- or ACh-directed intervention; (3) deficiency of the cortical salience network is sizable in subgroups of both disorders, and biomarkers such as eye blink rate and pupillometric data may predict the efficacy of targeting this underlying deficiency  Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral  deficits . In the As to the underlying neurobiological correlates, ASD has been linked to impaired function or structure of prefrontal cortex PFC; , amygdalConnectivity within well-defined networks in relation to ASD has been an area of great interest e.g., . SridharUnderstanding neural connectivity is an important first step in identifying how the brain effectuates important functions. However, a connection diagram of the brain should indeed be seen as a first step, and not as the final answer to comprehending how nervous systems realize function. One potential complication, as evidenced in animal studies, is that connectivity is dynamic and can change over time . Such dy1 Nervous systems have a wide range of such neuromodulatory systems  is an enzyme that catalyzes the conversion of DA into NE . Alterat in ADHD . For exae sample . In ASD,y a role . Furtherehaviors . Anotherand ADHD , others and ADHD . Senguptider see . Other e in ADHD . The oneociation . Other wociation . More spAs stated, PCs in the cerebellum have substantial projections to the LC . Notablyphasic mode in which phasic activity is relatively high and tonic activity is moderate and a tonic mode which shows the reverse pattern. When in phasic mode, metabolic resources are used to process task-relevant stimuli, and this improves current task performance. In contrast, when in tonic mode, metabolic resources are no longer used to focus on the task and is linked to distractibility. Importantly, in tonic mode, resources are used to identify salient stimuli in the environment  were found to be linked to expression of these genes, mainly in cerebellum, subcortical regions and frontal cortex, and this was predictive of ADHD. Importantly, these genes have been implicated in fetal and postnatal neurodevelopment including NAc, pointing toward involvement of epigenetic factors in the dysfunction of the DA system in ADHD  antagonism for risperidone). Both drugs decrease hyperactivity and irritability and both drugs seem to benefit the clinical global impression  or by additional direct agonism of the nicotine receptor  enhance especially NE transmission, but also 5-HT transmission; in both cases this is effectuated through reuptake blocking and thought to underly the antidepressant effect. Of note, they also block muscarinic ACh receptors which accounts for most of their undesired side effects . TCAs haAs reviewed, dysfunction of catecholaminergic and cholinergic neuromodulation play a role in the symptomatology of ASD and ADHD . ImportaDRD3, specific for ASD, and SLC6A2, specific for ADHD).Accumulating evidence suggests overlapping genetic contributions to potential dysfunction of the catecholaminergic and cholinergic systems in ASD and ADHD . HoweverOne further difference between ASD and ADHD in the dysfunction of neuromodulatory systems is the role of PCs. PC functioning is affected by especially by NE and ACh neuromodulatory systems, and has been implicated in ASD pathology, but less strongly in ADHD . TherefoDRD3 and SLC6A2 genes, in both ASD and ADHD populations. Other biomarkers may also be predictive in ASD. For example, pupillometry measures have been used in ASD to identify dysfunction of the LC-NE and NBM-ACh systems . Current medications prescribed for ADHD are effective in ADHD groups, but there is currently not enough evidence to suggest that these medications can consistently and effectively alleviate core symptoms in ASD groups. NErgic agents, MPH, and cholinergic agents should be investigated further using double-blind placebo-controlled trials to confirm the potential therapeutic value of these agents and to assess their neurobiological effects in ASD. Lastly, biomarkers such as pupillometry, eye blink rate, cognitive functioning and genetics should be investigated in relation to ASD to identify which medications may be most effective for a specific individual.Altogether, DA, NE, and ACh are important systems in ASD and ADHD symptomatology. Although the dysfunction of these systems show overlap between the disorders, there are also differences such as PC/cerebellar functioning and the involvement of specific genes (DK wrote the original draft of the manuscript. PD and JK provided the critical comments. DK and JK revised the manuscript. All authors contributed to the article and approved the submitted version.


Melanocortin 3 receptors (MC3R) and melanocortin 4 receptors (MC4R) are vital in regulating a variety of functions across many species. For example, the dysregulation of these receptors results in obesity and dysfunction in sexual behaviors. Only a handful of studies have mapped the expression of MC3R and MC4R mRNA across the central nervous system, with the primary focus on mice and rats. Because Syrian hamsters are valuable models for functions regulated by melanocortin receptors, our current study maps the distribution of MC3R and MC4R mRNA in the Syrian hamster telencephalon, diencephalon, and midbrain using RNAscope. We found that the expression of MC3R mRNA was lowest in the telencephalon and greatest in the diencephalon, whereas the expression of MC4R mRNA was greatest in the midbrain. A comparison of these findings to previous studies found that MC3R and MC4R expression is similar in some brain regions across species and divergent in others. In addition, our study identifies novel brain regions for the expression of MC3Rs and MC4Rs, and identifies cells that co-express bothMC3 and MC4 receptors within certain brain regions. As an initial approach to cataloging the expression of MC3R and MC4R in the hamster brain, we chose to focus on the telencephalon, diencephalon, and midbrain. We observed expression in 26 brain regions across these levels of the brain. We chose to categorize the distribution within these three levels of the nervous system due to their functionality and evolutionary conservation. Our findings bring novel insights of the distribution and the expression of MC3R and MC4R mRNA in the brain.2.2.1.n = 4) Syrian hamsters (Mesocricetus auratus) were purchased from Charles River Laboratories  at approximately 60 days of age (120–130 g). Females were housed individually in polycarbonate cages (50.8 × 40.6 × 20.3 cm). All animals were maintained on a reversed 14 h:10 h light/dark photoperiod (lights off at 1300 h). The animal room was maintained at a controlled temperature of 22°C, and food and water were available ad libitum. Subjects were ovariectomized (at approximately 90 days of age) and were approximately 140–150 days old at the time of tissue collection. All animal procedures were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals  and approved by the University of Minnesota Institutional Animal Care and Use Committee.Adult female  and sacrificed by rapid decapitation. Brains were extracted, frozen in optimum cutting temperature compound (VWR Scientific Products) and stored at −80°C. Brains were later sliced coronally on a cryostat at 14 μm thickness and sections were mounted on Colorfrost Plus slides . Some studies in rats and mice have provided an extensive analysis of range of the distribution of melanocortin receptor expressing neurons. Here, we limited our focus to the forebrain and midbrain with tissue collected from slices containing the prefrontal cortex through to slices containing the dorsal raphe nucleus. Slides were stored at −80°C until RNAscope procedure treatment.2.3.in-situ hybridization, the protocols for fresh-frozen sample preparation and Multiplex fluorescent v2 assay were followed according to manufacturer’s instructions . Briefly, slides were removed from −80°C storage and fixed in 10% neutral buffered formalin  for 15 min at 4°C.For Slides were rinsed twice in phosphate-buffered saline (9.8 g/l PBS), which contains 80 g/l sodium chloride, 14 g/l sodium phosphate dibasic, 3 g/l sodium phosphate monobasic, and 2 g/l potassium chloride (Fisher Scientific), at pH 7.4 and then dehydrated for 5 min each in 50, 70, 100, and 100% ethanol at room temperature. After air-drying for 5 min, hydrophobic barriers were drawn around the tissue to minimize loss of applied solutions. Slides were treated with Protease IV, incubated at room temperature for 30 min, and washed twice for 2 min in PBS. Two probes targeting Syrian hamster sequences were created by Advanced Cell Diagnostics  and used for the procedure: Mau-MC4R-C1 ; GenBank accession number  and Mau-MC3R-C2 ; GenBank accession number . Probes were applied such that each tissue received 75 μl of solution that contained a 50:50 ratio of probe diluent (Advanced Cell Diagnostics) and probe mixture. Each probe mixture contained a 50:1 ratio of C1:C2 . Tissues were incubated at 40°C for 2 h. Following incubation, tissues were washed twice for at room temperature for 2 min each with washing buffer solution  based on 2.4.A Stereotaxic Atlas of the Golden Hamster Brain  for all brain regions for each subject (resulting in a sample of four images for each brain region for each subject). Images were collected with a 20X/0.60 advanced correction system objective with a pixel distribution of 1,024 × 1,024 at a frequency of 8 kHz. A solid-state laser with 488 and 532 nm wavelengths and an ultra-high dynamic PMT detector was utilized to capture z-stack images with 1.5 μm spacing for a maximum of 15 steps. The pinhole size was 1 airy unit (AU), 2 frames were averaged, and optical zoom was 1.00X. 3D images produced were 550 × 550 × 14 μm.2.5.Images were analyzed using Imaris software  to investigate (1) the number of puncta of mRNA, (2) the number of cells that express MC3R and MC4R mRNA, and (3) the colocalization of MC3R and MC4R mRNA. First, the Spots feature was utilized to create a model of each channel. The estimated XY diameter was set at 0.650 μm. Next, the “quality” filter was applied, and the threshold was adjusted manually to include all spots that met the diameter in each channel. Then the “average distance to 3 nearest neighbors” filter was applied for all spots that passed the quality filter. The number of spots that were filtered into the model was then recorded for each of the MC3R and MC4R channels .3 were included in the surface model. Third, a “volume” filter was applied in order to include in the model only cells with a volume greater than 65.45 μm3. The number of cells that were filtered into the model was then recorded for each of the MC3R and MC4R channels. The criteria for cell surfaces excluded any non-specific binding that may be present, as the non-specific binding would not cluster in a way that meets the area and volume parameters that we have set  was calculated for each region and used to determine the strength of expression within a brain region . The fou3.3.1.In the telencephalon, there was a greater number of cells that expressed MC4R mRNA than MC3R mRNA in all the regions analyzed except the LS ; Table 1The LS had a greater number of cells that express MC3R mRNA compared to MC4R mRNA, whereas the MS had a greater number of cells that express MC4R mRNA compared to the number of cells that express MC3R mRNA. The number of cells that express MC3R mRNA within the LS was moderate, with an average of 45.6 cell counts . The numThere was a low average number of cells that expressed MC3R within the DEn, while the number of cells that express MC4R mRNA was high , allowing researchers to isolate sex itself from other behaviors such as locomotion . ErectilThe BNST and the PAG of the hamster had equal expression levels of MC3R mRNA with that of the rat whereas both regions along with the LS of the hamster had lower levels of MC3R mRNA expression compared to that of the mouse . The MPOStudies have identified the involvement of MC3Rs and MC4Rs in reward circuits. Antagonists acting on MC3Rs and MC4Rs result in alterations in reward behaviors and locomotor activity . A rewarThe MC3R mRNA expression levels of the hamster VTA are lower than that reported for the rat . The hamPrimary functions of melanocortin receptors are feeding and sex behavior, which was part of the basis for our focus on the forebrain and midbrain distribution of melanocortin receptors. Feeding circuits also involve hindbrain regions, suggesting that melanocortin receptors could be located more caudally. Further, melanocortins affect pain regulation which is modulated by hindbrain and spinal cord circuits. In fact, prior studies have demonstrated MCR distribution in the hindbrain and spinal cord. This study was conducted in four ovariectomized female subjects. Consequently, we do not know if there are differences in the number of MC3R or MC4R expressing neurons across the female’s reproductive cycle or whether there are sex differences for Syrian hamsters. Previous studies have found quantitative sex differences in MC3R and MC4R expression as well as sex differences in function. In the VTA, activation of MC3R neurons results in a significant decrease in food intake for females but not males, whereas the inhibition of MC3R neurons significantly decreases food intake in males but not females . A knockOne limitation of our study is that many regions are large with heterogenous subdivision and circuits. The MCR cell counts across regions will certainly vary depending on the particular subdivision sampled and where the sampling lies in a rostral-caudal dimension. We intentionally matched our sampling within restricted regions across subjects to ensure that each region was analyzed at the same level. The utility of our study and prior mapping studies is that it points the way for investigators interested in a specific brain region to perform detailed analyses of receptor distribution and circuits. To conclude, this study has added novel insights into the distribution of MC3R and MC4R across the central nervous system of a non-murine rodent and demonstrates the high level of conservation of melanocortin receptors among rodent species. Further, our study has advanced our understanding of the co-expression of MC3R and MC4R cells in the rodent nervous system, paving the way for future functional analyses of the melanocortin system.The original contributions presented in the study are included in the article/The animal study was reviewed and approved by University of Minnesota Institutional Animal Care and Use Committee.MH, RM, and JB designed the research. MH, AK-J, AP, GF, NS, and JB performed research. MH wrote the paper. All authors contributed to the article and approved the submitted version.This work was supported by grants from the National Institutes of Health to RM (R01 HD100007 and R01 HD100007-03S1). JB was supported by an NIH Training Grant (T32 DA007234) awarded to Paul Mermelstein.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.


Attention deficit hyperactivity disorder (ADHD) is a condition manifested in childhood and includes symptoms, such as attention deficit, impulsivity, difficulty in concentrating, hyperactivity, and aggressive behavior. Along with the negative effects of ADHD symptoms on individuals, various psychological factors are thought to be associated with these symptoms. However, ADHD in emerging adulthood is not rare and can be conceived as the continuation of discomfort that might emerge during childhood and adolescence. Our mediation study involved 613 undergraduate students in İstanbul. The participants filled out demographic information forms: Adult Attention Deficit and Hyperactivity Disorder Self-Reported Scale (ASRS), Perceived Stress Scale and Beck Depression Inventory (BDI). Our work stressed those positive correlations found among ASRS, Perceived Stress Scale, and BDI scores. Analysis by Model 4 of Hayes Process Macro and Sobel tests showed that ADHD symptoms predicted both perceived stress and depression scores of the participants and dimensions of perceived stress acted as partial mediators in the positive relationship between ADHD and depression scores. By negatively affecting the quality of life and the academic performance, psychological problems have always been among the crucial health issues involving university students . Among tContrary to the common belief stating that ADHD is a childhood problem, studies have shown that it may also endure in adolescence and adulthood . ADHD inThe literature showing that the social and academic demands cause stress among university students; thus, the current study aims to examine how adult ADHD symptoms, as a potential source of academic strain, predict stress levels and depressive symptoms of undergraduate students. We also intend to investigate the mediating role of stress on the relationship between ADHD and depressive symptoms.As a neuropsychiatric disorder, ADHD is often associated with childhood. However, recent follow-up studies have shown that between 50% and 70% of ADHD incidences persist into young adulthood . ADHD prAdults with ADHD suffer from concentration and focusing problems more than they do from hyperactivity and impulsivity. ADHD-related difficulties cause social and academic problems. Concentration problems and difficulties in completing assignments are the common problems observed in the academic lives of those with ADHD. Due to such problems, individuals with ADHD cannot improve their academic and social skills and are subject to the risk of experiencing problems in their adulthood in terms of career, family, and social life. Some psychiatric disorders that are comorbid with ADHD, such as mood disorders, anxiety, and learning disability, can also increase that risk .Self-reported studies in the literature for ADHD, conducted in different samples show that university students have a significant level of ADHD symptoms. Weyandt et al. , clarifyWhen students start university in their late adolescence, their life becomes more demanding than ever. They may find it hard to adapt to a new academic and social life. Besides, most of them leave their family for the first time in their lives and move to another city alone. Adaptation to a new system, leaving family, the pressure their family exerts on them to succeed, financial problems, academic competition, course load, and exam stress cause excessive stress among the students . ExcessiSome of the problems experienced by adolescents or adults with ADHD, such as planning and organizing problems, time management difficulty, and difficulties in setting priorities, focusing, paying attention, and completing tasks can create extra stress . Those kH1. Stress scores of university students are expected to increase as their ADHD symptoms increase. In other words, ADHD scores of students predict their stress scores.Depression is characterized by several symptoms including bad mood, lack of pleasure and interest, interrupted sleep, changes in appetite, fatigue, low energy, poor concentration, indecision, feeling of worthlessness, slowing down, or agitation of physical movements ,20,21. TMany studies have been carried out regarding the effect of depression on the academic performance of students and the results unveiled that depression could cause an approximately half-point decrease of GPA (grade point average). It was also found that students missed classes or exams due to depression . DepressStressful situations and life events are triggering factors that can lead to depression. Quite a few studies were conducted on university students regarding the relationship between stress and depression. Dahlin et al.  carried Academic performance is one of the most fundamental qualifying requirements for students to graduate from school ,29. TherAdult ADHD-related problems may create extra stress on university students in addition to their academic load and university transition . ExcessiH2. Depression scores of university students are expected to increase as their ADHD symptoms increase. In other words, ADHD scores of students predict their depression scores.H3. Depression scores of university students are expected to increase as their stress scores increase. In other words, stress scores of students predict their depression scores.H4. Through increasing their stress levels, ADHD symptoms increase the depression scores of the students. In other words, the stress level acts as a mediator in the positive relationship between ADHD and depression.The sample of the study consists of 613 students studying at a foundation university located in İstanbul. Among those 613 participants, 234 are males and 379 are females. The average age of males is 21.39 (s.d. = 1.914), while the average age of females is 20.90 (s.d. = 1.634). The data were collected from various faculties’ students: 267 students from the Health Science faculty, 47 students from Social Sciences and Humanities faculty, 87 students from the Engineering faculty, 32 students from the Management Sciences faculty, 71 students from Law School, 24 students from the Education faculty, 26 students from Architecture faculty, 38 students from Applied Sciences faculty, and 21 students from Fine Art faculty. All participant students were volunteers. Demographics are presented in The participants were asked to fill in a demographic information form, including questions about their gender, age, faculty, department, class, GPA, and familial matters.21-item Beck Depression Inventory was used to identify the depression levels of the participants. Participants were asked to choose one of the four responses, ranked from zero to three, for each item to indicate how they have been feeling in the last week including the day they were asked to respond to the questionnaire. The scale was translated into Turkish by Hisli in 1989 and the validity and reliability of the scale were ensured . For theThe 14-item Perceived Stress Scale, developed by Cohen et al., was used to measure participants’ stress levels . The scaThe scale was developed by Kessler and Üstün in 2004 . It was Before the application of the research, ethical approval was obtained from the ethics committee of the university in which the research would be conducted. After the approval was obtained, the students were informed about the research, and questionnaires were delivered to those who volunteered to participate. All participants’ informed consent was gathered at the beginning of the survey. It took about 10 min for each participant to fill in the questionnaires.Data gathered from this study was analyzed by using IBM SPSS Statistics 22 program. Pearson’s r correlation coefficients were calculated to find out the relationships between the variables. Besides, the Process Macro Analysis Method version 3.3, developed by Hayes was used to test the research model. Hypotheses were tested by “parallel multiple analysis” model 4 defined by Hayes . This anForemost, the relationship among the variables was measured through Pearson’s r correlation analysis. The findings are given in The bootstrapping Process Macro method, which allows multiple mediation models to be tested, was used for testing the mediator roles of the sub-dimensions of the perceived stress variable. In the multivariate relationship model tested, adult ADHD scores were the independent variable and Beck Depression Inventory scores were the dependent variable. Insufficient self-efficacy perception and perception of stress/distress, which are the sub-dimensions of the perceived stress scale, were analyzed as mediating variables. The 95% confidence interval was taken into account to test whether indirect effects were significant in the model.B = 0.354, SE = 0.05, t = 7.304, p < 0.001). Adult ADHD explains 8% of the variance in depression scores . When mediator variables, namely stress sub-dimensions, insufficient self-efficacy perception and stress/distress perception were added to the model, 25% of the variance was explained . The effects of adult ADHD scores on depression scores were significant when the effects of mediator variables were controlled . In the findings of the bootstrapping analysis, it was found that the total of indirect effects that adult ADHD had on depressive symptoms through the perceived stress sub-dimensions was significant . When the mediation effects of the perceived stress sub-dimensions were examined, insufficient self-efficacy perception , stress and discomfort perception  variables were found to partially mediate the relationship between adult ADHD and depressive symptoms significantly, as the confidence intervals do not contain a zero value in between.The findings showed that the overall effect of adult ADHD symptoms on depression scores was significant (p < 0.001) and perceived stress/distress  were also supported by the Sobel Test. All these findings fully support the first three hypotheses and partially supported the fourth hypothesis we have put forward. Findings regarding the hypothesis testing, including the mediation analysis, are presented in The Sobel Z scores were calculated for the mediating role of perceived stress subdimensions. The mediating roles of perceived insufficient self-efficacy  and their limited capability of assessing themselves accurately  ,60.Overall, depression may result from various factors. Among those factors, adult ADHD should be taken into consideration; and the mental health professionals providing counseling services at universities should conduct questionnaires and clinical interviews to test whether the students applying for counseling have any ADHD symptoms. Besides, considering the studies reporting that ADHD is an important risk factor for giving up education life, it is important to identify and support those students who have problems regarding adult ADHD . The couEventually, this work highlighted how ADHD symptoms in emerging adulthood can increase the stress levels of university students and therefore indirectly affect depressive symptomatology. This indirect effect of ADHD on depression is mediated by both perceived stress and perceived insufficient self-efficacy.


COVID-19 has been causing huge disruptions in mental healthcare services worldwide, including those related to ADHD. Some consequences of the pandemic, such as virtual schooling and remote work, as well as increased telemedicine, have posed new challenges for ADHD diagnosis and treatment. In this narrative review, we summarize existing COVID-19 and ADHD literature especially focusing on ADHD diagnostic during the pandemic and treatment adherence.The databases searched were: PubMed, PsycINFO, EMBASE, Google Scholar and medRxiv. We included all English language articles and preprints that reported on medication/pharmacological treatment among the terms “ADHD” and “COVID-19” resulting in a total of 546 articles. The final search was done on Dec-23 2021. We selected fifteen articles focusing on the challenges of ADHD diagnostic during COVID-19 pandemic.Of the fifteen studies included, most were cross-sectional and perspective pieces. Most of them discussed that individuals with ADHD present risk factors that may make them more vulnerable to health negative consequences of the pandemic, which in turn may have an impact on treatment efficacy and adherence. Telemedicine is also addressed as a potential powerful instrument on monitoring ADHD treatment.Despite the challenges posed by the pandemic on monitoring ADHD treatment, the available literature stressed that the current scenario also may offer new opportunities that could lead to the development of individualized treatment interventions, such as the remote monitoring of symptoms. Attention deficit hyperactivity disorder (ADHD) is a prevalent, impairing condition that is frequently comorbid with other psychiatric disorders and creates a substantial burden for the individual, their family, and the community . PositivCOVID-19 has been causing huge disruptions in mental healthcare services worldwide . A signiRecently investigators and clinicians have sought to examine the possible impact of COVID-19 pandemic on patients with ADHD –13. CortIn this narrative review, we summarize existing COVID-19-related literature pertinent to ADHD, with a special focus on treatment adherence, integrating recent research findings. We also provide a discussion on the potential implications of the reviewed studies for the ADHD field and provide future research directions.This narrative review aims to identify which are the most relevant findings regarding the new challenges in diagnosing and treating patients with ADHD during the COVID-19 pandemic. We believe that the biggest challenge is to establish how to perform the best remote monitoring of patients with ADHD, ensuring good adherence to treatment. Using telemedicine more efficiently will be a crucial point in achieving good adherence to ADHD treatment.In this review, we use a narrative approach , 17. TheA total of fifteen studies were identified and included in this narrative review. There were no randomized controlled trials on this specific topic. Most of the included studies were viewpoint/expert opinion, case–control studies and treatment guidelines. The mental health outcome of the COVID-19 pandemic is increasingly recognized as a relevant, worldwide public health concern . In indiSibley et al. used a survey to assess the top problems reported by adolescents and young adults with ADHD during the COVID-19 pandemic . The mosNon-weiler et al. investigated the prevalence of emotional and behavioral problems assessed by parent reports among children and young people with ADHD between April and 2nd June 2020 . They foIt has also been postulated that quality of sleep may be profoundly impacted by COVID-19, and that children and adolescents with ADHD may be particularly vulnerable to disturbed sleep during the pandemic , 25. BreResults from a survey study showed that restrictions imposed by the pandemic may also impact children and adolescent’s well-being. The authors found children and adolescents had less time spent on physical exercising, less time to engage on outdoor activities and less enjoyment in those activities while time spent on television, social media, gaming, sad/depressed mood, and loneliness were increased compared to pre-pandemic era .The implementation of a medication protocol in ADHD with periodic assessment of symptoms, real-life functional benefits and adverse effects is imperative for adjusting pharmacological treatment to optimize outcomes . During n = 11) suggesting this modality as a viable method to provide pharmacologic treatment for children with ADHD . Among the reasons informed by parents for stopping an ADHD medication were taking a break during school holidays, not requiring medication due to school closure/remote learning and stopping one ADHD medication to start another. Only 11% of parents reported difficulties in purchasing ADHD medication. Among the reasons, the two most common were: (1) medication not available on the stock and (2) difficulties on getting the prescriptions.Changes in medication treatment patterns during the pandemic were investigated by Sciberras et al. in a survey study with 213 parents of children with ADHD in Australia  during the COVID-19 pandemic  using cell phone cameras . The autIt has been postulated that ADHD-related difficulties are associated with a high risk of having COVID-19 . To testTo further investigate the effect of ADHD stimulant pharmacotherapy on preventing COVID-19 infection in patients with ADHD, the authors carried out subsequent comparisons between patients treated for ADHD, patients not treated for ADHD and a control group of individuals with no ADHD diagnosis . ResultsIn this paper, we review the current state of knowledge on the potential impact on the COVID-19 pandemic for the clinical management of ADHD. The key findings in this review are: (1) Patients with ADHD had a higher prevalence of psychiatric symptoms as a consequence of the COVID-19 pandemic. There was also a greater negative impact on learning, social life, and quality of life; (2) The risk of discontinuing the treatment of ADHD, with negative consequences for patients and families, has brought a warning to medical societies. This concern led to the creation of guidelines and strategies to maintain the correct monitoring of these patients at a distance; (3) Research findings showed a higher frequency of ADHD diagnoses in samples from COVID-19 patients. Correct treatment of ADHD was associated with a lower risk of SARS Cov 2 infection. The main findings summarized above describe the many negative consequences associated with stopping ADHD treatment. They also point to the difficulties of evaluating and diagnosing new symptoms or comorbidities. Finally, the findings suggest the need to maintain remote monitoring, especially using telemedicine. Several studies have been published to evaluate the effectiveness of telemedicine. Positive results regarding its effectiveness, but also its limitations, have been investigated for decades , 32. TheThe use of telemedicine for the diagnostic evaluation of patients with a clinical picture suggestive of ADHD has already been investigated. In a review on the subject, carried out before the COVID-19 pandemic, the authors indicated the effectiveness of telemedicine in the evaluation of possible new cases of ADHD. These findings are especially important for the evaluation of patients in remote areas where specialists are not present . HoweverMonitoring ADHD patients during the COVID-19 pandemic represents a challenge for specialists and general practitioners. The findings indicate that the discontinuation of psychotherapy and pharmacological treatment is very harmful to patients. Guidance on the best way to monitor patients remotely has been provided by several medical associations. However, we still do not have enough specific research on the effectiveness of remote monitoring of patients with ADHD. At this time, further research to investigate the role of telemedicine in the follow-up of patients with ADHD  is needed.The COVID-19 pandemic has brought new challenges to the treatment of patients with ADHD. Initial findings showed that ADHD patients during the COVID-19 pandemic are more vulnerable to a variety of negative outcomes. These patients present greater difficulties in studies and school performance. The various findings presented in this review point to the need to adequately maintain the treatment of individuals with ADHD. Strategies for treatment adherence are even more necessary. The use of telemedicine, in particular, proved to be a very relevant measure.Our review has limitations that need to be highlighted. The choice of narrative review instead of systematic review is the main limitation. We recognize that a systematic review would be the best choice. However, carrying out a systematic review in our study center in such a short time proved to be unfeasible. Another limitation that should be pointed out is inherent to the selection of reviewed articles. In order to focus mainly on articles on monitoring patients with ADHD in the context of the COVID-19 pandemic, we excluded articles and reviews that included samples from patients diagnosed with other mental disorders. Finally, another important limitation was the inclusion of only articles published in English during the years 2020 and 2021.Future research should focus on conducting prospective follow-up studies of patients with ADHD and their families, using telemedicine strategies. These new studies may provide more detailed data on both short-term and long-term adherence to ADHD treatment. They will also be able to point out which changes and adaptations will be necessary for better monitoring of patients with ADHD.DS defined the scope of publication interpreted data, wrote the manuscript, screened the studies, analyzed the data, provided a critical review of the manuscript, revised critically the work providing substantial input, and gave final approval of the version to be published.DS has served on the speaker’s bureau and/or acted as consultant for Shire/Takeda; he has received travel awards to participate in scientific meetings from this company.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.


No group differences were present during either of the unisensory conditions. Differences in the integration areas, particularly in the right-hemispheric parietal brain regions, were found in those with ADHD. These alterations may correspond to impaired attentional capabilities when presented with multiple simultaneous sensory inputs, as is the case during a multisensory condition.Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that exhibits unique neurological and behavioural characteristics. Our previous work using event-related potentials demonstrated that adults with ADHD process audiovisual multisensory stimuli somewhat differently than neurotypical controls. This study utilised an audiovisual multisensory two-alternative forced-choice discrimination task. Continuous whole-head electroencephalography (EEG) was recorded. Source localization (sLORETA) software was utilised to determine differences in the contribution made by sources of neural generators pertinent to audiovisual multisensory processing in those with ADHD versus neurotypical controls. Source localization techniques elucidated that the controls had greater neural activity 164 ms post-stimulus onset when compared to the ADHD group, but only when responding to audiovisual stimuli. The source of the increased activity was found to be Brodmann Area 2, postcentral gyrus, right-hemispheric parietal lobe referenced to Montreal Neurological Institute (MNI) coordinates of X = 35, Y = −40, and Z = 70 ( Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that is defined by specific behavioural characteristics . PromineThere are well noted neural alterations associated with ADHD, relating to both functional and structural characteristics that are unique to this population. This includes diffuse reductions in grey matter found throughout the cortex, including parietal, temporal, frontal, and occipital brain regions ,7,8,9,10The neural substrates that underly attention and multisensory processing share many commonalities, including the superior colliculus, fronto-parietal, and temporo-parietal networks . This, tMSI is a form of neural processing that describes how the nervous system combines incoming sensory information, integrating this information in order to create a coherent perception of the surrounding environment . AlthougMSI is associated with functioning in specific brain regions and is thought to occur in the early phases of stimulus processing. The literature suggests that the integration of audiovisual multisensory inputs occurs prior to 200–250 ms after stimulus onset . For insNeuroimaging techniques have illustrated underlying neural substrates that are imperative to many forms of sensory processing, including audition and vision, and those involved in the integration of such inputs for audiovisual processing . CertainOur previous research found that there were statistically significant and unique differences in how young adults with ADHD process AV multisensory inputs when compared to neurotypical controls ,32. ThesOur previous work assessing MSI in ADHD involved the analysis of ERPs using super-additive models ,32; the The research question that this work aims to answer is whether there are specific neural structures involved in the processing of multisensory inputs in adults with ADHD that differ from neurotypical controls? It is hypothesised that there will be differences in which cortical regions are primarily involved in sensory processing, particularly those involved in MSI, such as the parietal brain regions.n = 10, three females) had a mean age of 23.7 ± 3.3 years, with a mean age of diagnosis being 13.7 ± 7.7 years old. The neurotypical control group  had a mean age of 21.7 ± 1.8 years old. Those with ADHD reported the average age of diagnosis to be 13.7 ± 7.7 years old.This research received approval from the Ontario Tech University Research Ethics Board (REB), and participants provided written informed consent prior to participation. This study was performed according to the principles set out by the Declaration of Helsinki for the use of humans in experimental research. The participants included in the present analysis are identical to those whose multisensory ERP EEG peaks were assessed in McCracken et al., 2020 . The parThe Adult ADHD Self-Report scale (AASRS-v1.1) checklist questionnaire was completed by all participants prior to participation; this was undertaken to quantify their ADHD symptomology. The AASRS-v1.1 consists of 18 questions that are highly correlated to the diagnostic criteria set out by the DSM-IV  and are A unisensory auditory stimulus was presented in front of the participant from speakers placed bilaterally from the computer screen. The stimulus was a female verbalisation of the word red, blue, or green . This stimulus choice was due to the importance of semantic congruence between sensory cues in multisensory conditions  to enhance multisensory processing . AuditorA unisensory visual stimulus consisted of a circle  filled with the colour red, blue, or green. The circle was on a black background and lasted for a duration of 250 ms.The multisensory stimulus consisted of the described auditory and visual conditions occurring simultaneously. The auditory and visual components were always semantically congruent. For example, when the blue circle appeared, the “blue” verbalisation was emitted, etc. The conditions were never semantically incongruent. Please refer to To measure MSI, a two-alternative forced-choice discrimination task was utilised that consisted of three unique stimuli that were semantically congruent. For example, a female verbalisation of the word “red” was paired with a red circle, or verbalisation of the word “blue” was paired with a blue circle. Similar paradigms have been used previously to quantify multisensory processing ,31,48,49A Waveguard™ 64-electrode EEG cap  was used to collect surface brain electrical activity in response to performing the forced-choice tasks in each of the three sensory conditions. The Waveguard™ cap was connected to a TMSi REFA-8 amplifier  with 64 EEG channels, four bipolar channels, and four auxiliary channels and was collected through Advanced Source Analysis Lab™ (ANT Neuro) at a sampling frequency of 2048 Hz. The EEG data were processed offline using ANT 4.10.1. The artifacts resulting from muscle activity and/or blinking were removed. A band-pass filter was utilised, with a low cut-off of 1.6 Hz to remove the constant slow-wave activity and a high cut-off of 45 Hz to remove any artifacts from the surrounding electrical equipment; this was performed with a slope of 24 db/octave, which was applied to individual data sets. Artifact rejection was performed by excluding waveforms that exceeded ±100 μV. The electrodes still containing significant noise  were interpolated for the relevant individual participant using the nearest surrounding eight electrodes. This was undertaken on an individual data set basis. The EEG data were then averaged per condition into 600 ms epochs (−100 to 500 ms) surrounding stimulus onset, giving three averages for each participant . Current source analyses were performed on the entire epoch, meaning they were examined from −100 to 500 ms post stimulus onset, although with a particular interest within the 0–250 ms latency window, as previous multisensory work suggests early integration is specific to latencies prior to 250 ms post stimulus.The primary objective of the research reported here was to build upon prior novel findings , and wasSource localization was performed to assess the neural areas of greatest activity in response to each sensory condition, allowing for a comparison between groups. This was performed using standardised low-resolution brain electromagnetic tomography (sLORETA) software . sLORETAp = 0.05. The statistical analysis was conducted within sLORETA’s built-in statistical tool [t-test, converting collected EEG data into t-values for each time frame, comparing two independent groups, in this case, ADHD vs. controls. This was performed for 1229 time frames, which is 600 ms with a sampling frequency of 2048 Hz. This was performed using SnPM, which adjusts for multiple comparisons using Fisher’s random permutation test with 5000 randomisations [t-value threshold, providing the t-critical, where once a value in the time-domain exceeds this threshold, a computation occurs within the software that localizes the area responsible for this neural activity while also providing the associated statistical significance . Additionally, ASA Lab™ (ANT Neuro) sLORETA tool was used to create graphics of each group’s neural activity during the timeframe that was established using the sLORETA analysis software for all sensory conditions. Graphics of localized activity were created and have been included for each condition  and each group. The statistical significance was set to cal tool ,54. sLORisations . The sofp = 0.048). This can be seen in The comparison of multisensory responses between the ADHD group and the controls concluded that neural generators differed significantly between groups when presented with an audiovisual multisensory stimulus. The area of greatest difference in neural activity between groups was right hemispheric, Brodmann area (BA) 2, postcentral gyrus, parietal lobe . No timeframe surpassed t-critical. However, although not meeting statistical significance, the area of greatest difference between groups when presented with the visual unisensory condition occurred at BA 22, superior temporal gyrus, left-hemispheric temporal lobe . Please see There were no significant differences between groups when presented with the visual unisensory condition (p > 0.05). No timeframe surpassed t-critical. However, although statistical significance was not met, the auditory unisensory condition resulted in an area of greatest difference in BA 10, superior frontal gyrus, left-hemispheric frontal lobe . Please see There were no significant differences between groups when presented with the auditory unisensory condition  has a fundamental role in processing afferent sensory input, particularly somatosensory input . AdditioThe right-hemispheric difference specific to BA 2 is consistent with our previous work on ADHD. Specifically, we previously reported that when presented with an AV multisensory stimulus, neurotypical controls had a more pronounced negative ERP over right-hemispheric centro-parietal brain regions, specifically electrodes CP4 and P6, when compared to young adults with ADHD . This prAdditionally, the previous analysis yielded results showing that those with ADHD had greater activity in areas related to multisensory integration in parietal-occipital brain regions  at an earlier latency, from 110 to 130 ms . AlteredThe current study detected altered activity within BA 2 specific to the right hemisphere in young adults with ADHD. BA 2 is found within the parietal lobe. The parietal lobe is generally associated with sensory processing and contains the primary (S1) and secondary (S2) somatosensory cortices . ParietaAnatomically, S1 is located at the postcentral gyrus of the parietal lobe, and an altered structure may have important functional implications and vice versa. Imaging studies have noted that children with ADHD have significantly reduced cortical volume in the right parietal lobe . A previAn fMRI study with both clinical and behavioural importance found right-parietal dysfunction in young boys with ADHD . SpecifiThose with ADHD responded differently at a neural level in response to the multisensory condition; this unique difference in those with ADHD was found in BA 2, the parietal lobe. It should be noted that this difference was unique to the multisensory condition, and no group differences were found in either unisensory condition. This further suggests that the unique neural activation is a result of the multisensory nature of the task. One potential explanation for these differences in sensory perception between the groups could be that those with ADHD-like traits have a reduced ability to discern when multiple stimuli occur simultaneously . This isAlthough limited literature has assessed MSI in ADHD, previous work has shown that there are differences in the way that adults with ADHD process and respond to multisensory inputs at both the behavioural and neurological levels. MSI is fundamental to how individuals experience the world, as many environments are multisensory in nature. For instance, at any given time, individuals are presented with auditory, visual, and tactile stimuli. Therefore, alterations to multisensory processing may have fundamental implications for how one processes, perceives, and responds to their environment, potentially impacting day-to-day activities such as working and socialising. These differences can be observed using neurophysiological and behavioural measures. For instance, historically, S1 was often thought of as unisensory but has more recently been associated with being the initial site for processing associated with MSI ,73. TherThe previous unique neural alterations relating to MSI in ADHD were predominantly localized to parietal, occipital, and central electrode regions ,32. ThesA tenet of multisensory processing that can have a profound influence on the integration effect is related to the allocation of attentional demands to each sensory component that encompasses the multisensory stimulus. This suggests that the level of selective attention can modulate MSI . For insThis study focused on university-aged individuals recruited from the university campus. Thus, limiting the generalizability of findings to those outside of this age group, such as children or adolescents with ADHD. A limitation of the current work is the utilisation of the entire group of adults with ADHD and that it does not specify or compare by subtype . In the future, work should address this and explore potential differences in multisensory processing between ADHD subtypes. Additionally, although utilising sLORETA software provides a strong basis for the interpretation of neural generators, it should be noted that this involves pairing collected EEG data with a standardised average MRI. Therefore, although this is an acceptable, validated, and cost-effective technique to interpret neural activity, the MRI used for localization is not definitively reflective of each individual’s specific neural structures. In the future, incorporating fMRI techniques and individual scans would enhance the findings noted above.The present data analysis yielded results indicating that young adults with ADHD process AV multisensory stimuli differently than controls. In particular, neural differences were localized to the right hemisphere over parietal regions associated with BA 2. No statistically significant differences in neural generator activity between groups were found for either unisensory condition. Therefore, the differences noted herein are unique to multisensory processing in those with ADHD. This result may have important implications for how they process and respond to multisensory inputs while also reflecting alterations to attentional capacity when more than one stimulus is presented at a time. Our previous work utilising the same data found that neurotypical controls had greater activity in this brain region, specifically over right-hemispheric centro-parietal (CP4 and P6 electrodes) brain regions from 160 to 180 ms , suggest


The aim of this systematic review is to identify and appraise pharmacological options available for management of ADHD in pregnant women and adverse effects of ADHD medications on pregnancy and foetus .Systematic review of prospective or retrospective cohort studies, available on this topic till September 2021 after PubMed and MEDLINE search, carried out by 2 reviewers independently. The preliminary search was conducted in March 2021, though the reviewers carried out timely cross-referencing as required. All cohort studied except those with ambiguous methodology were included. The data were further extracted using Microsoft excel after Quality Assessment was completed using NewCastle Ottawa Scale. A narrative synthesis was undertaken as meta-analysis was not feasible owing to heterogeneity between studies included.Eighteen Cohort studies were included (N = 28227 pregnant women with ADHD) of which 16 were deemed as good quality. Multiple confounders were identified.The review noted that use of stimulants/non-stimulants reduced symptoms and improved functionality in these expectant mothers. Findings from our review overall indicate low risk of developing malformations with ADHD medications, with only Methylphenidate (notably cardiac) and Modafinil showing slightly increased though statistically significant risk. We also noted slightly increased risk for reduced Apgar scores, abortions on maternal request, pre-eclampsia and preterm births. There was no conclusive association noted between neuro-developmental delay or future ADHD in baby/child.A case-by-case approach needs to be adopted for every patient, looking at how ADHD affects daily functioning and balancing that against adverse pregnancy outcomes. Also, innovative practices like drug holidays, as required medications, drug free trial while planning pregnancy etc. will help practitioners streamline the treatment of this group of patients better. Issues like research being restricted to certain countries, small sample size, record-based analysis, issues in ascertaining adherence, confounding factors, ethical conundrums etc. were noted.


IntroductionThe current study aimed to investigate the overlap between symptoms of lipedema and attention-deficit/hyperactivity disorder (ADHD). Lipedema is a condition that causes abnormal fat accumulation and inflammation in the legs and buttocks, often accompanied by edema and pain. ADHD is a common condition characterized by difficulty paying attention and controlling behavior, affecting the social, academic, and occupational quality of life. The study’s primary objective was to assess the prevalence of ADHD symptoms in a population of women with lipedema symptoms and compare the clinical characteristics.MethodThe study used a lipedema screening questionnaire and the Adult Self-Report Scale (ASRS-18) to assess the prevalence of ADHD in a sample of 354 female volunteers with or without a prior lipedema diagnosis.ResultsOf the lipedema group, 100 (77%) were ASRS positive, and 30 (23%) were ASRS negative. In the group without lipedema, 121 (54%) were ASRS positive, and 103 (46%) were ASRS negative, with a relative risk of 1.424 (p<0.0001).ConclusionOur results demonstrate a positive correlation between lipedema and ADHD and suggest that targeted strategies to improve clinic attendance for individuals with ADHD may improve lipedema treatment outcomes. Patients with lipedema symptoms are more likely to have ADHD symptoms. The current study examined the overlap between lipedema symptoms and attention-deficit/hyperactivity disorder (ADHD). Lipedema is characterized by the abnormal accumulation of fat in the lower limbs, which may be accompanied by complaints of pain and edema when standing up ,2. The cADHD is a common condition affecting the quality of life for children and adults in social, academic, and occupational contexts. The underlying causes of ADHD are not fully understood. Still, proposed mechanisms include glial activation, neuronal damage and degeneration, increased oxidative stress, reduced neurotrophic support, altered neurotransmitter metabolism, and blood-brain barrier disruption. There is growing evidence that inflammation may also play a role in ADHD. However, this evidence is mainly from observational studies showing a strong co-occurrence of ADHD with inflammatory and autoimmune disorders, studies evaluating serum inflammatory markers, and genetic studies. Inflammation may be an essential factor in the development of ADHD, but further research is needed to confirm this . ADHD isThis study aims to evaluate the correlation between lipedema and attention-deficit/hyperactivity disorder (ADHD) in the Brazilian female population. The primary objective is to assess the prevalence of ADHD in women with lipedema characteristics. Understanding the prevalence rates of ADHD in this population has direct clinical implications since this subgroup has inflammatory triggers. Additionally, the study will describe the clinical characteristics of those presenting ADHD compared to those not meeting the criteria.www.lipedema.org.br), and it was administered to 354 volunteers with or without a prior lipedema diagnosis. The participants were women over 18 years old who registered on the online survey platform and volunteered. The sampling method used is convenience sampling. Participants who did not digitally sign the consent form were excluded. Questionnaires completed in less than 300 seconds were excluded. A mean time was previously calculated as three minutes [We used the 10-question lipedema screening questionnaire previously published  and the  minutes  for the Screening probability of lipedemaThe measure was based on our group’s previously proposed lipedema screening questionnaire  and cutoScreening probability of ADHDAll eligible subjects completed the Adult Self-Report Scale (ASRS-18) in Portuguese , an 18-iStatistical analysishttps://www.medcalc.org (2023)) and Wizard 2.0.12 . This study complies with the National Health Council standards set out in resolution 196/96 regulating research involving human beings. It also adheres to the Helsinki Declaration. The institution’s ethical committee approved the study, and all subjects signed an informed consent form before participation.A sample size of 152 questionnaires was calculated to achieve a 95%CI, considering a 5.5% margin of error. Statistical analysis was conducted after the consistency of the data had been checked manually and automatically with software developed explicitly for this analysis using Xojo 4.1 . Descriptive statistics and frequencies were calculated. Correlations between questionnaire variables were assessed using the chi-square test (z-score), Mann-Whitney, and Student’s t-test. We adopted a p<0.05 level of statistical significance for the correlations. The software used for data analysis is MedCalc® Statistical Software version 20.211  ) age of the whole study population was 36.8 ±9.1 SD) years, while the mean (±SD) age of volunteers with the lipedema diagnostic criterion was 38.2 (±9.6 SD) years, which is equivalent. The mean (±SD) height of the whole study population was 163.4 (±6.3 SD) cm, while the mean height of volunteers with the diagnostic criterion was 163.3 (±6.6 SD) cm, which is equivalent. Within the lipedema group, 100 (77%) were ASRS positive, and 30 (23%) were ASRS negative. Of the subgroup of volunteers without diagnostic criteria for lipedema, 121 (54%) were ASRS positive, and 103 (46%) were ASRS negative (Table  SD yearsThere was a 35% dropout rate (less than 300 seconds and fill-out mistakes) during the completion of the questionnaire , and the mean time taken to respond was 482 seconds. It was observed that 36.7%±5  of the study population met the lipedema diagnosis criteria, while 62.4%±5  were ASRS positive, and 37.6% (n=133) were ASRS negative, with a relative risk of 1.424   or obese (50%). Another study observed that 67.5% of women with lipedema had BMI greater than 25 kg/m2, with a mean BMI of 27 kg/m2. Elevated BMI makes diagnosis more difficult because of the complexity of differentiation from common obesity [The similarity between the population groups studied was demonstrated. There was a difference regarding weight and BMI, probably because lipedema can interfere with body weight. Dudek et al. . also us obesity . Althoug obesity , our stuOur study demonstrated a positive correlation between lipedema symptoms and ADHD symptoms, meaning that as self-reported lipedema symptoms increase, ASRS symptoms also tend to increase Figure .ADHD exhibits a diversity of phenotypes, including inattention, impulsivity, or a combination of both. These specific features may not always be accounted for in surgical literature and may potentially influence surgical outcomes .It is also worth noting that individuals with ADHD are at increased risk of comorbid mental health disorders, including mood and anxiety disorders . AnxietyMocanu et al. . found tAs a standard of care, lipedema surgery should only be considered for patients with controlled mental health conditions who can understand the risks and benefits of surgery and participate in necessary follow-up care.The current self-report measures for assessing the presence of ADHD and lipedema have limitations. Lipedema inflammation is cyclical , so it mThe lipedema screening questionnaire and ASRS, available in the public domain, can provide a quick and cost-effective means of predicting the likelihood of a diagnosis but cannot give a definitive diagnosis. A previous study found that the ASRS can distinguish between individuals with previously diagnosed ADHD recruited from disability services . AnotherPsychiatric disorders are mainly characterized by symptoms and not observable signs, which require interpreting the patient’s report and translating it into diagnostic terms. Self-reflection and self-evaluation can also be problematic and may lead to over- or underreporting of symptoms. Additionally, psychiatric symptoms or clusters of symptoms are often not specific to a particular disorder. Different patient assessment strategies, such as open, structured, or semi-structured interviews, can significantly influence the results, and even small changes in question-wording can affect the responses. While standardized interviews are considered the gold standard for psychiatric diagnosis, efforts to minimize these limitations can involve additional patient-related data, often improving diagnostic accuracy .The sample of volunteers in this study consisted of women who registered on an online survey platform seeking information about lipedema. As a result, this population may not represent the general population, and there may be selection bias. It is possible that individuals with more severe or complex cases of lipedema or ADHD may have been more motivated to participate, which could have influenced the results. Compared to a previous population study that showed that 12.3% of volunteers met the criteria for lipedema diagnosis, our study found a higher rate of 36.7% [These limitations suggest further research on the potential correlation between ADHD and lipedema. That way, we can better understand lipedema’s neurophysiological mechanisms. This includes exploring inflammation’s role in ADHD and lipedema and developing methods for better measuring low-grade chronic inflammation . IncorpoIn this study, we found a higher prevalence of self-reported symptoms of ADHD in patients with lipedema symptoms. This suggests a potential overlap between the two conditions and highlights the importance of screening for ADHD in patients with lipedema. Implementing targeted strategies to improve clinic attendance for individuals with ADHD may improve lipedema treatment outcomes and reduce variability in results. Furthermore, incorporating mental health considerations, including routine screening for ADHD, into treatment approaches for lipedema may be beneficial. While the limitations of self-reported symptoms should be considered, our study provides insights into the potential correlation between lipedema and ADHD.


Although attention deficit hyperactivity disorder (ADHD) in children is rising worldwide, fewer studies have focused on screening than on the treatment of ADHD. Most previous similar ADHD classification studies classified only ADHD and normal classes. However, medical professionals believe that better distinguishing the ADHD–RISK class will assist them socially and medically. We created a projection-based game in which we can see stimuli and responses to better understand children’s abnormal behavior. The developed screening game is divided into 11 stages. Children play five games. Each game is divided into waiting and game stages; thus, 10 stages are created, and the additional waiting stage includes an explanation stage where the robot waits while explaining the first game. Herein, we classified normal, ADHD–RISK, and ADHD using skeleton data obtained through games for ADHD screening of children and a bidirectional long short-term memory-based deep learning model. We verified the importance of each stage by passing the feature for each stage through the channel attention layer. Consequently, the final classification accuracy of the three classes was 98.15% using bi-directional LSTM with channel attention model. Additionally, the attention scores obtained through the channel attention layer indicated that the data in the latter part of the game are heavily involved in learning the ADHD–RISK case. These results imply that for ADHD–RISK, the game is repeated, and children’s attention decreases as they progress to the second half. Attention deficit hyperactivity disorder (ADHD) is a chronic disease characterized by distraction, hyperactivity, and impulsivity ,2. EarlyThe first example is a method that uses the inertial measurement unit (IMU) and machine learning. After attaching two IMU sensors to the children’s ankles and waist, IMU data obtained while the children and their parents were consulted by a doctor for approximately 1 h were used as input data. In the case of the machine learning algorithm, a support vector machine (SVM) was used, and an accuracy of 95.12% was finally obtained . The secThe abovementioned ADHD screening studies also have limitations. Since the method using the IMU simply measures the amount of exercise performed by children, there is insufficient information to perform various analyses. Studies using fMRI or EEG can be objectionable to children because hospitals use specialized equipment and children must go inside the MRI machine or have EEG electrodes placed on their heads.Additionally, for data acquisition, children visit a hospital, in an unfamiliar environment, and participate in a data acquisition experiment using various sensors or testing equipment. If nonadult children are subjected to an experiment in an unfamiliar environment, they may not engage in their usual behavior. Because ADHD is diagnosed on the basis of problem behaviors observed while observing children’s usual behavior, the accuracy of diagnosis can be further improved by conducting an experiment in an environment in which children’s usual behavior can appear naturally .To overcome this limitation, in this study, the data used were acquired through robot-led games in schools and children’s centers familiar to children. Furthermore, in this study, the robot-led ADHD screening game used was developed by ADHD specialists and child psychologists to naturally draw appropriate stimuli and responses from children through robots and games.Moreover, children can participate in this experiment without any objection because of the content of games, robots, and the familiar environment for children.The four methods mentioned above are currently the most studied methods for classifying ADHD using machine learning or deep learning. In the case of the IMU introduced first, children feel uncomfortable because they wear the IMU device on their body for a long time. However, unlike the IMU method, since this system allows children to play games for a short period of time without wearing equipment, relatively objective data can be obtained because they can focus only on the game while obtaining data, unlike the IMU method. In addition, in the case of EEG and fMRI data, data acquisition must be performed in a hospital and this requires very expensive and special equipment. However, in the case of this system, children’s data can be obtained with simple equipments in a child-friendly environment because data are obtained from a school familiar to children. Finally, the methods using questionnaires or test scores, which are currently used most often in hospitals, have the disadvantage of low objectivity. However, since the developed system uses data obtained while all children play the same game in the same environment, objective results can be extracted using objective data. All references mentioned in this paper classify only ADHD and normal groups. However, ADHD–RISK was classified in this study. In this study, unlike other papers, high accuracy was obtained by including the ADHD–RISK group as well as the ADHD and normal groups.It is difficult for clinicians and professional teachers to identify the ADHD–RISK class, so it was rarely used in previous studies. However, ADHD–RISK is necessary for systematic ADHD screening. In this paper, the ADHD–RISK class was added to develop a systematic screening tool for clinicians and professional teachers.The contributions of this paper are summarized below.In this paper, ADHD, ADHD–RISK, and Normal groups were screened with high accuracy using skeleton data and deep learning obtained through relatively simple measurements through interesting simple games in a child-friendly natural environment without expert intervention.Unlike previous studies, the ADHD–RISK class, which is not easy for clinicians to distinguish, was added, and high classification accuracy was obtained.ADHD, ADHD–RISK, and Normal groups were classified with high accuracy using only skeleton data, which was previously not used for ADHD classification.When classifying ADHD–RISK children through the channel attention layer, it was verified that classification becomes more helpful toward the later part of the game.The detailed structure of this paper is as follows. The introduction section introduces the existing methods for screening ADHD. The materials and methods section introduces the materials used in this study and the methods proposed. The results section describes the experimental results. The discussion section describes the discussion in this study. Finally, the conclusion section presents a conclusion.Children play the game to collect skeleton data. The game can be divided into three main stages. When the game starts, the game screen is displayed on the floor using the projector. A total of five games are played, starting with the pre-explanation stage, in which the robot explains the overall game. Each of the five games is divided into two stages: a waiting stage in which the robot performs a game demonstration and a game stage in which children directly participate in the game after the robot game is over.The robot-led game played by children is described as follows. Nine number plates are displayed on the floor using the projector. The robot randomly passes through the printed numeric keypad. At this time, the child memorizes the number board passed by the robot in the waiting area, and when the robot returns to its original position, the child must pass the number board passed by the robot in the same way according to the start signal given by the robot .Furthermore, children must perform one more mission while passing the number board. While the children are playing the main game, characters appear on the screen, as shown in The children participating in the game were recruited from 8- to 13-year-old elementary school students from Seoul, Korea. We tried to reduce environmental variables as much as possible by playing the game in schools or child research centers near where children live, rather than in unfamiliar environments such as hospitals or research institutes. From 2019 to December 2021, skeleton data of children were obtained at three elementary schools and one child research center where children attend. All children who participated in the game had their parents’ consent, and they were all administered the DSM–ADHD scale, CBCL (Child Behavior Checklist), and K–ADHDS . After seeing the results of the above three screening tests and the video of the children who played the game, four doctors then discussed and diagnosed each child’s assignment into normal, ADHD–RISK, or ADHD groups, and the results were used as the children’s label. As shown in Skeleton data of children acquired through a robot-led ADHD screening game were used as input data. The input data were divided for each stage obtained from the total game. A total of 11 input data types were used: six waiting stage data, including the pre-explained stage, and five game stage data. Since each stage takes a different amount of time for each child to complete the game, a data structure was created based on the longest frame for each stage, and then all data were matched to the structure. In the case of data from a frame shorter than the reference data structure, the remaining frames were filled with 0.A total of 32 skeleton data points were provided by Kinect Azure; however, in this study, 18 joints with excellent skeleton acquisition quality were used, as shown in As shown in In this study, an attention layer for the stage level was proposed to improve recognition performance. This was inspired by the channel level attention layer proposed in the reference paper . First, That is, during the classification, the importance of features for each stage is randomized and derived. Finally, the attention score is multiplied for each stage of the input of the attention block, and weighting is applied according to the importance of the feature. The application of attention to each stage proposed in this paper can induce learning, allowing the recognition model to prioritize important stages. Moreover, the model not only can improve recognition performance but also provides an interpretation of which stage is an important stage in classifying ADHD based on the attention score applied during classification. it is the truth label, pi is the Softmax probability, and iw is the weight of the loss function.ReLU was used as the activation function, and the resulting reshaped feature was passed through the classification layer to classify normal, ADHD–RISK, and ADHD groups. Since the number of input data used varies, a weighted cross entropy loss function, as shown in Equation (2), is used to prevent overfitting and improve performance. In the case of weights, the inverse of the input data ratio was used, and for verification, the leave-one-person-out cross-validation method was used. The learning rate of the model used in the paper above is 0.001, the epoch is 20, the batch size is 50, and when four LSTM layers are used, the hidden size of LSTM is 128. Total number of parameters is 15,185,521.F1 score, FNR, FPR, and FDR. The formulas are as follows [Normal, ADHD-RISK, and ADHD were classified using an LSTM-based deep learning algorithm and skeleton data of children acquired through a robot-led game for screening children’s ADHD. In this paper, the model was evaluated using accuracy, specificity, sensitivity,  follows :(3)AccurTP (true positive) represents the result of predicting the correct answer that is actually true as true; FP  is the result of predicting the correct answer that is actually false as true; FN  is the result of predicting that the correct answer that is actually true is false; and TN (true negative) is the result of predicting that the correct answer that is actually false is true.In the above equations, F1 score, FPR, FDR, and FNR for each class. Accuracy, sensitivity, specificity, and F1 score for the normal class were 98.99%, 100%, 97.57%, and 99.14%, respectively; for the ADHD–RISK class they were 98.15%, 93.92%, 100%, and 96.86%, respectively; for the ADHD class they were 99.16%, 100%, 99.06%, and 96.35%, respectively. Also, FPR, FDR, and FNR for the normal class were 0.024, 0.016, and 0, respectively; for the ADHD–RISK class they were 0, 0, and 0.06, respectively; and for the ADHD class they were 0.009, 0.07, and 0, respectively. Finally, the classification accuracy of the three classes was 98.15%. In our previous study, which was conducted without using the channel attention layer, a classification accuracy of 97.81% was obtained for the classes.In this study, an ADHD screening algorithm was developed using the skeleton data of children acquired through games for ADHD screening. Moreover, the channel attention layer was used to improve the performance of the algorithm by verifying which parts of the waiting stage and the game stage developed for the classification of normal, ADHD–RISK, and ADHD are helpful for ADHD screening and classification. To verify the significance of each stage, first, the attention score obtained through the channel attention layer was analyzed. Following the completion of learning, attention scores were generated for each stage for the 596 children who participated in this study. Consequently, no attention score showed a significant difference for each stage. Based on the above results, it was determined that all stages had a uniform influence on the learning outcomes. Second, the attention layer was analyzed for each class rather than an overall analysis. In this analysis, as shown in Consequently, in the case of the normal and ADHD classes, no significant deviation in all stages was observed. However, in the case of the ADHD–RISK class, significant results were confirmed for both the waiting and game stages. In both the waiting and game stages, it was confirmed that the upper distribution rate of the attention score increased from the start of the third game.To confirm the results more easily, the above results are shown in a graph, as shown in When the children in the ADHD–RISK group were observed while playing the game, it was confirmed that they played the game similarly to the normal group. For this reason, it is judged that it is not easy for doctors to distinguish between ADHD–RISK and the normal group. However, in the result of the attention score obtained through the channel attention layer, the characteristics of ADHD–RISK were found as shown in In this study, a deep learning algorithm based on skeleton data and LSTM acquired through games was developed for ADHD screening of children, and normal, ADHD–RISK, and ADHD class classification studies were conducted. The channel attention layer was used to improve the performance of the algorithm and to analyze the developed game. Using the final algorithm developed in this study, the classification accuracy of the three classes was 98.15%. Furthermore, by analyzing the attention score obtained through the channel attention layer, it was confirmed that in the case of ADHD–RISK, children’s concentration decreased toward the end of the game. If in the future we design a new game that requires more concentration in the second half based on the above results, it is expected that it will aid in ADHD screening by increasing the classification accuracy of the ADHD–RISK class, which has lower classification accuracy than other classes.