To quantify goal-directed behavior and ADHD symptoms in naturalisticconditions, we developed a virtual reality task, EPELI (ExecutivePerformance in Everyday LIving), and tested its predictive, discriminant andconcurrent validity.We collected EPELI data, conventional neuropsychological task data, andparent-ratings of executive problems and symptoms in 38 ADHD children and 38typically developing controls.r = .57) and ADHD symptoms(r = .55) pointed to its concurrent validity.EPELI showed predictive validity as the ADHD group exhibited higherpercentage of irrelevant actions reflecting lower attentional-executiveefficacy and more controller movements and total game actions, bothindicative of hyperactivity-impulsivity. Further, the five combined EPELImeasures showed excellent discriminant validity (area under curve 88 %),while the correlations of the EPELI efficacy measure with parent-ratedexecutive problems (We provide a proof-of-concept validation for a new virtual reality tool forecologically valid assessment of ADHD symptoms. Attention deficit hyperactivity disorder (ADHD), characterized by the symptoms ofinattention, hyperactivity, and impulsivity, is among the most commonneurodevelopmental disorders, with an estimated 5.9% world-wide prevalence atchildhood . ADHD diADHD is associated with several adverse outcomes such as impairments in quality oflife, emotional and social impairments, and educational underachievement , aswellThus far, the most widely used VR paradigm in ADHD research has been the continuousperformance task (CPT), where the participant responds to relevant objects and avoidresponding to other pre-designated objects in an ongoing stimulus stream. Theapplication of CPT has been a logical step, as it has been the most consistentcognitive test method in differentiating children with versus without ADHD  and used it to study how ADHD children performdaily chores in an environment akin to those where their symptoms occur. EPELI isinspired by studies examining real-world executive functions in prefrontal patientse.g.,  by orienBy using the rich data EPELI provides, we operationalized measures that reflect thekey features of ADHD symptomatology, namely attentional-executive function problemsand hyperactivity-impulsivity. Our primary aim was to examine whether participants’performance and behaviors while playing EPELI show (i) predictive validity bydifferentiating between ADHD children and typically developing controls, (ii)discriminant validity by differentiating individual children based on their groupstatus (ADHD vs. controls), and (iii) concurrent validity by being associated withparent-rated ADHD symptoms and executive function deficits.In the preregistration of the study (AsPredicted.org #31918), we set more specifichypotheses that pertain to the three objectives listed above. Concerning predictiveand discriminant validity (points (i) and (ii)), we hypothesized that ADHDparticipants would exhibit a lower percentage of relevant actions out of all actionsthan typically developing (TD) controls. We also expected that movement trajectorieswhen navigating in the apartment would be longer in ADHD than in TD due to poorerplanning and execution skills, and that the sensors attached to the HMD and thecontroller would detect higher levels of motion in ADHD participants (head andcontroller hand movements), indicating hyperactivity. ADHD participants were alsoexpected to show more actions overall, reflecting impulsivity. Based on prior VRstudies, these group differences were expected to be particularly pronounced inscenarios with more distracting stimuli . MoreoveIn total, 47 children with ADHD and 68 TD controls participated in this study.For children with ADHD, the inclusion criteria were (a) ADHD diagnosis withpredominantly hyperactive/impulsive or combined inattention andhyperactive/impulsive subtype (F90) set by a licensed physician following theICD-10 criteria , (b) ageThe participants with ADHD were recruited at the Helsinki University Hospital byadvertising the study at a Child Psychiatric Unit with handouts and phone calls,and by advertising the study through Finnish ADHD Foundation contact channels,at the Espoo City Child Psychiatric Unit, the Vantaa Family Counselling Unit,and a private clinic in Espoo (ProNeuron LTD). The eligibility of each child toparticipate in the study was initially checked on the first contact  with the parent. For the ADHD group, all diagnoses were controlled forby checking medical documents  duringthe measurements and the other inclusion and exclusion criteria from the parentquestionnaires before or after the measurements. The TD children were recruitedfrom schools at Espoo and Kirkkonummi either by inviting the children toparticipate after a lecture where they had been informed about the study or bysending a recruitment letters to the parents via schools’ electronic messageboard. Also for the TD group, the eligibility was initially probed on the firstphone call or email with the parent, and later controlled from the parentquestionnaires, where the parents were asked to list any diagnoses of theirchild. The study was reviewed and approved by the Ethics Committee of theHelsinki University Hospital. All participants gave their informed consentaccording to the Declaration of Helsinki. All participants were compensated withtwo movie tickets.To gather information on any possible major concurrent comorbid psychiatric orneuropsychiatric conditions, the children in the ADHD group and their caretakerswere interviewed with suitable modules  from the Finnish version of the diagnostic instrumentMINI-KID Interview for Children and Adolescents 7.0 . In thisFor their ADHD symptoms, 28 ADHD participants had a methylphenidate prescription,one had a lisdexamfetamine prescription, one had an atomoxetine prescription,and eight were unmedicated. The medication was not taken on the measurement days(24-hour washout period). In addition, six ADHD participants had other ongoingmedication .http://www.peilivision.fi/). An Oculus Go HMD  and its handcontroller were used for playing the game, while the experimenter monitored taskperformance using a Samsung Galaxy Tab S3 tablet. Navigating in the environmentwas conducted by pointing at a waypoint circle on the floor with a handcontroller and simultaneously pressing a button, which resulted in teleportingto that waypoint. Participants used the same button for interacting with theobjects. During game play, motion tracking sensors in the goggles as well as inthe controller captured the participants’ movements.EPELI (link to a video) was designed with equal contribution by ML, JS, and ESbased on similar previous studies in other patient groups e.g., . ImplemeSupplemental Methods for the floor plan). In the game, childrenperform 13 short everyday scenarios. Before the actual game begins, there is apractice session where the participants practice navigating in the environment,interacting with the objects, and monitoring time by using a watch that becomesvisible when the participant looks down to the controller and turns its facetoward him/herself. A cartoon dragon character in the game guides the childthrough the practice session and returns to give instructions for each taskscenario. Before each task scenario, the dragon gives orally a list of subtasksto be conducted .Presentation of the 13 task scenarios was counterbalanced so that every otherparticipant conducted them in reversed order. Each task scenario includes fourto six subtasks (four subtasks in the task scenarios at the beginning and theend) covered by instructions of 30 to 66 words. In total, there are 70 tasks, 52of which can be completed at any time, 13 to be completed at a certain time(time-based tasks), and 5 after an external cue . The child is instructed tocomplete the subtasks in the given order, except for the time- and event-basedsubtasks, but the completion order does not affect the scoring. One taskscenario lasts maximum of 90 seconds but ends earlier if all subtasks arecorrectly performed. Seven (for participants conducting the task scenarios inforward order) or six (for participants conducting the task scenarios in reverseorder) task scenarios are embedded with auditory , as well as audiovisual  distractors. Inaddition, these conditions contained more task-irrelevant objects. Thedistracted conditions were counterbalanced across the participants at the sametime when the order of the task sets was changed. Distractors were on during thewhole task set in the distracted conditions . Total duration of EPELI isapproximately a maximum of 35 minutes. After the EPELI session, the participantsperformed Repetition task where they verbally repeated instructions similar tothose that the dragon gave in EPELI (eight sentences with a length of 18–54words). This task assessed the role of the memory component in EPELIperformance, as the prospective memory paradigm called for keeping theinstructions in mind.The VR environment in EPELI is an apartment that has a children’s room, livingroom, kitchen, open adult bedroom, utility room, and toilet/bathroom . To review the experiences of theparticipants and to acquire information about potential confounds, participantsanswered to a shortened version of the Presence Questionnaire 3.0 . The child’s familiarity with thetasks was assessed by asking the question “From a scale of 1 to 7, how much haveyou performed similar tasks in real life?”Parents evaluated their child’s ADHD symptoms, possible executive functionsdeficits, and possible psychiatric symptoms using the ADHD Rating Scale-IVADHD-RS; , the Behaire 3.0 ,the Simaire 3.0 , a gaminSupplemental Methods.The conventional neuropsychological tasks included in the study included theSimilarities and Matrix reasoning subtests of the Finnish version of theWechsler Intelligence Scale for Children WISC-IV; , ContinuMeasurements of the TD participants were conducted either in dedicated rooms inschools or at the university facilities . ADHDparticipants were measured at university (ABL or the Åbo Akademi University),apart from one participant who was measured at school. Each participantunderwent two measurement sessions lasting about 60 minutes each. The firstsession comprised WISC-IV Matrix Reasoning, EPELI and its related questionnaires, and the Repetition task, always in the sameorder. EPELI was played while in a chair that rotated 360° to help theparticipants in turning in the game easily and safely. Before starting the game,head set position and sound loudness level were adjusted if needed. The secondsession comprised WISC-IV Similarities, WISC-IV Digit Span, the clock task, andcomputerized tasks . The order of CPT, SRT,F&C, and the Cruiser tasks was counterbalanced using a Latin square designto control for possible fatigue, while the other tasks were performed at fixedpositions in the task battery. The WISC-IV Digit Span was always performedbetween Cruiser’s practice and experimental phases. Thus, one possible taskorder was WISC-IV Similarities, SRT, the second trial of the Clock Task, CPT,the third trial of the Clock Task, HEXE practice phase, F&C, HEXE repetitionof the plan, the fourth trial of the Clock Task, Cruiser practice phase, WISC-IVDigit Span, Cruiser experimental phase, and HEXE execution phase. The twosessions were conducted either on the same day (separated by a break of at least15 minutes) or on separate days. The MINI-KID interview was conducted after thesecond session, preceded by a break of at least 15 minutes.R version4.0.3  in the dependent variables but none werefound. In total, the amount of data removed was 9.0%.A small part (2.4%) of the task performance data was lost due to technicalfailures and because of one participant with ADHD refusing to perform the threeremaining tasks in the second measurement session. In the following analyses,first, two participants (one from the control group and one from the ADHD group)were removed from the Cruiser task due to purposefully crashing into other carsinstead of avoiding them because they felt that this was more fun than the taskthey had been given. Second, any participants who were not able to repeat theprospective memory task instructions after Cruiser, HEXE or the Clock task wereexcluded from the corresponding analysis. Third, any possible participantsperforming near chance level  in CPT,F&C, or HEXE were removed from analyses of that task. Fourth, all univariateoutliers (±3 SD’s from the group mean) in the dependent variables of the mainanalysis were excluded. Fifth, the data was checked for possible multivariateoutliers . For the event-based subtasks, only thosecompleted within 10 seconds after the target event were treated as correct. Forthe time-based subtasks, only those which were completed within 10 secondsbefore or after the target time were taken as correct. The final number ofvariables was reduced by examining their pairwise correlations in the controlgroup and removing one variable from each pair when the correlation was .85 ormore. The variables remaining after this procedure included Total score(correctly performed subtasks), Task efficacy , Navigation efficacy , Controller motion (controller angularmovement during task performance), and Total actions , time-based subtask score, number of clock checks, and event-basedsubtask score. Of these variables, the first five were regarded as the mainvariables  and distractors (on/off) on the EPELI variables were testedusing two-way analysis of variance. Furthermore, three-way analysis of variancewith playing order as the third independent variable was performed, but as theseanalyses yielded very similar results, those results are not shown. The effectsof scenario-to-scenario task progression on EPELI variables were examined withlinear mixed models. Based on Bayesian Information Criterion, random interceptmodel was the best fitting error covariance structure for all dependentvariables. The scenario-to-scenario variabilities of the EPELI variables weretested using t-tests for group effects. The group differenceson the conventional neuropsychological tasks were examined usingt-tests. Furthermore, Bayes factors were calculated for allthe effects mentioned above. Based on visual inspection, all the assumptions ofanalysis of variance were met.Group differences on the background variables were tested withDiscriminant validity of each EPELI variable was assessed bycalculating the area under curve (AUC) from the receiver operatingcharacteristic (ROC) curve. A cutoff point with the highest percentage ofcorrectly classified cases was determined by Youden’s index and the sensitivityand specificity of the variable at this cutoff was reported. Similar analyseswere performed for the conventional neuropsychological tests. To evaluate themultivariate classification capacity of EPELI and CPT, logistic regression wasapplied separately to the main dependent variables of each test, and theclassification value of the resulting variables were examined the same way.Concurrent validity was assessed by calculating Pearson’scorrelation coefficients over all participants between the EPELI variables andquestionnaires. Similar correlation analyses were performed between the EPELImeasures and conventional neuropsychological tests, as well as between thelatter measures and the questionnaires, but these were considered as secondaryanalyses.Supplemental Table 4). There were no group differences in thegaming experience, perceived familiarity of the tasks, or overall presenceexperiences. Both groups were able to reliably name the objects that wereincluded in EPELI (object naming task), which was taken to reflect that theywere familiar with the vocabulary used in the game. The participants reportedvery few negative experiences in the simulator sickness questionnaire.The background characteristics of the participants are presented in The box plots of EPELI measures per group are presented in Supplemental Figure 1), there was a main effect of time onTask efficacy , Navigation efficacy, Controllermotion , andTotal actions . In line with analysis of variance  andNavigation efficacy  the ADHD group showed stronger decline, and onTotal actions  the ADHD group exhibited stronger increaseover time. There were group differences also in the scenario-to-scenariovariability (SD) of Task efficacy , with the TD group demonstrating morevariability. Since Task efficacy is the percentage of relevant actions outof total actions excluding moving actions, a separate analysis for thevariabilities of its constituent measures was conducted to be able tointerpret the variability in Task efficacy. There was no group difference inthe number of relevant actions, but total actions excluding moving actionsyielded a group difference with the ADHD group demonstrating morevariability .Thus, the group difference in Task efficacy is caused by more variability intotal actions in the ADHD group. Furthermore, the ADHD group demonstratedmore variability in Controller motion  and Total actions. There were nogroup differences in Total score or Navigation efficacy variability.Regarding scenario-to-scenario progression  than the AUC pointestimate for CPT RT variability (.85). Considering the AUC estimates of EPELIand the conventional neuropsychological tasks together, the highest estimatesare those of EPELI logistic regression analysis, EPELI Task efficacy, CPTlogistic regression analysis, and CPT RT variability, which did not differ fromeach other (p > .05). AUC obtained from EPELI Task efficacywas significantly higher than in most of the conventional neuropsychologicaltasks, except Digit span, CPT RT variability, F&C switching cost, and HEXEongoing errors.The AUCs and cutoff values based on Youden’s index for the conventionalneuropsychological tasks and for the logistic regression analysis utilizing allfive CPT variables at the same time are presented in rabsolute value = .312–.574). For EPELI’s Total score, Task efficacy, andNavigation efficacy, this correlation is negative, so that higher performance inthese measures is associated with fewer problems with executive function andlower ADHD symptom scores. For Controller motion and Total actions, thedirection is the opposite.The correlations of EPELI with the BRIEF and the ADHD-RS questionnaires acrossall participants that were used to examine concurrent validity are presented inp < .05). In the FDR corrected statistics, there werealso no differences between the correlations of EPELI Task efficacy and BRIEFcorrelation versus the correlations between of BRIEF and CPT omission errors,CPT commission errors, and SRT mean RT.Supplemental Table 3. The Repetition task was associated withall EPELI measures correlating positively with Total score, Task efficacy andNavigation efficacy in EPELI. Both reasoning subtests (Similarities and Matrixreasoning) from WISC-IV correlated positively with EPELI Total score, butSimilarities correlated also with both EPELI efficacy measures. Cruisermonitoring was positively associated with EPELI Total score, while the totalnumber of correct task responses in HEXE shared a positive correlation withController motion and Total actions in EPELI.The correlations between the main EPELI measures and conventionalneuropsychological measures not yielding group differences are presented inNeurodevelopmental disorders such as ADHD do not fall into categorical cognitivedomains , but ratThe five EPELI measures selected for the final analysis were assumed to reflectADHD-related symptoms and key aspects in executive functions required in thetask. As regards to predictive validity, the expected group differences werepresent in all five measures . AccordiOut of the five selected EPELI measures, Task efficacy was a particularlyimportant variable in the present analyses. Representing the relative percentageof relevant actions out of all actions, it is related to selective attention,which is typically defined as focusing on a target object while not reacting toirrelevant ones. However, while traditional attention measures often address aspecific attentional component in a simplified context, this EPELI measurecovers various aspects of the participant’s interactions with the environment, coming closer to the diagnostic definitions of inattention as itmanifests itself in everyday life. The closer match to the diagnosticdefinitions was expected to boost the predictive validity of EPELI Task efficacyand result in stronger correlations with the subjective questionnaires than whatis seen with conventional neuropsychological tasks, and such findings wereindeed observed. Our global inattention measure showed robust group differences,was informative in predicting the group status of individual participants andwas strongly associated with ADHD symptoms and everyday EF dysfunction.Supplemental Table 2). Previously, it has been suggested thathyperactivity would be most clearly observed in cognitive tasks where the levelof stimulation is low . The results provided clear support for the hypothesis, furthershowing that the number of these impulsive actions fluctuated more over time inthe ADHD participants more than in TD participants. One can question whetherthese kinds of impulsive actions triggered by a potential immediate reward wouldbe more representative of the daily problems that ADHD children face than, forinstance, the ability to inhibit their response to a non-target letter in acontinuous sequence of stimuli . Our impulsivity measure bears agreater resemblance to delayed reward tasks where the target that triggersimpulsive behavior is motivating e.g., . A keyaSupplemental Table 3). Total score resembles the performancemeasures previously used in naturalistic prospective memory tasks such as theVirtual Multiple Errands Test where the participants simply repeated task lists similar to those that thedragon provided in the game, which indeed was the case . One explanation for this could be thatEPELI tasks are highly familiar and may not prompt within-task strategydevelopment in the same way as novel tasks do .Our study provides novel behavioral evidence that naturalistic VR is a reliablemethod to assess and quantify real-life attention and executive function deficits inADHD. Compared to more classical paradigms, advantages of this approach includeopportunities to (a) measure complex behavioral patterns in situations resemblingthose where the symptoms occur, (b) capture volitional behaviors reflecting typicalbehavior in open-ended situations that mimic real-life situations more closely, (c)provoke particular symptoms with specific experimental manipulations , and (d) quantifying the natural pace of participant’smotion with sensor technology. Regarding the participants’ experience, using agame-like paradigm with varied tasks and rich stimuli is probably more convenientand less tedious than simplistic tasks with restricted stimuli. Indeed, thisassumption is supported by our findings that both ADHD and TD children on averagerated playing EPELI as a highly enjoyable experience (see The present proof-of-concept study showed that EPELI has predictive validity bydifferentiating between ADHD children and typically developing controls,discriminant validity by differentiating individual children based on their groupstatus, and concurrent validity by being significantly associated with parent-ratedproblems in managing situations with high cognitive demands in real life. Besidesshedding light on the naturalistic behavior of ADHD children in daily situations,this study opens new avenues for the objective measurement of ADHD symptoms. Takentogether, these results suggest that measuring everyday attentional-executivedeficits linked to ADHD symptoms is possible with our new EPELI task. We hope thatthese findings will facilitate the development of naturalistic approaches for theassessment of neurodevelopmental disorders.Click here for additional data file.Supplemental material, sj-pdf-1-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-2-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-3-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-4-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-5-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-6-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention DisordersClick here for additional data file.Supplemental material, sj-pdf-7-jad-10.1177_10870547211044214 for QuantifyingADHD Symptoms in Open-Ended Everyday Life Contexts With a New Virtual RealityTask by Erik Seesjärvi, Jasmin Puhakka, Eeva T. Aronen, Jari Lipsanen, MinnaMannerkoski, Alexandra Hering, Sascha Zuber, Matthias Kliegel, Matti Laine andJuha Salmi in Journal of Attention Disorders


Enterobacter, Escherichia coli, and Clostridium strains, and markedly lower abundance of beneficial bacteria, such as the Bifidobacterium and Ruminococcus strains, than the control group. The heat maps of metagenomics indicated that each group was separated into distinct clusters, and the processed food and ADHD groups showed significantly lower α-diversity of gut microbiota than the control group. In these groups, the concentration of acetate or butyrate in the feces was significantly lower than that in the control group. These results may indicate that imbalanced diets can disturb the colonic microbial balance and are likely to become a potential risk factor for the prevalence of ADHD.We investigated the impact of dietary patterns on the gut microbiota and concentration of short-chain fatty acids in the feces of Korean elementary school students. The dietary intake and ADHD assessment of 40 Korean elementary school students were analyzed using a dish-based semi-quantitative food frequency questionnaire. Analysis of gut microbiota and short-chain fatty acids composition were performed using the real-time polymerase chain reaction, metagenomics, and gas chromatography methods. The dietary patterns of participants were divided into four groups: healthy, processed food, fish and shellfish, and meat. The participants were also divided into two groups according to their ADHD scores: 0–30, control group; over 30, ADHD group. The ADHD score of the processed food group was significantly higher than that of the healthy group. The processed food and ADHD groups showed significantly higher abundance of harmful bacteria, such as the  Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders in childhood . It is aBifidobacterium strains, are associated with the increase of a dopamine precursor, indicating that the gut microbiota and their metabolites may modulate the release of neurotransmitters related to ADHD [The genetic information of gut microbiota is known as the second genome of humans and several studies have reported that its abnormal alteration is associated with various diseases –10. The  to ADHD .Lactobacillus and Bifidobacterium strains, resulting in the prevention and improvement of diseases through a balanced composition of the gut microbiota [The composition of the gut microbiota is affected by various factors, such as region, race, and diet . Dietarycrobiota . SCFAs ccrobiota . Alteratcrobiota , 24. Milcrobiota  revealedcrobiota . TherefoThis study was approved by the Sahmyook University Ethics Committee (2-7001793-AB-N-012018037HR). The dietary intake of 40 Korean elementary school students (11 years old) was analyzed using the Can Pro 4.0  database and a dish-based semi-quantitative food frequency questionnaire (FFQ), which was filled out by the parents. Based on the frequency of intake, the participants were divided into nine categories , and based on the amount of intake, they were divided into three categories . The FFQ consists of 107 food items commonly consumed by Korean school-aged children. These foods are classified into 30 major groups by slight modifications to the method described by An et al. : rice, 2We estimated the ADHD scores of participants using Korean ADHD rating scale (K-ARS), which is commonly used in various institutions to assess the ADHD scores of children in Koreans. K-ARS, which was filled out by parents and class teachers, uses a four-point scale  and consists of 18 questions, of which nine each are related to the attention deficit and hyperactivity disorders, respectively. The ADHD group was identified using the criteria of Lee  and the We investigated the height, weight, body mass index (BMI), waist-to-hip ratio, and blood pressure of the participants. We also recorded the disease and medication history of the candidates and collected their feces to analyze the gut microbiota composition and fecal SCFA concentration.5′-GCAGGCCTAACACATGCAAGTC-3′; reverse, 3′-CTGCTGCCTCCCGTAGGAGT), Bifidobacterium , Lactobacillus , Clostridium , Enterobacter , and E. coli  strains based on the method described by Han et al [qPCR was performed using DT-prime 5  with the power SYBR Green PCR master mix  and specific primers to measure the total number of bacteria  and 805R (5′-CAAGCAGAAGACGGCA-TACGAGAT-X-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGACTACH-VGGGTATCTAATCC-3′). PCR was conducted using Master Mix and PTC-200 Peltier Thermal Cycler  using the following program: initial denaturation step at 94°C for 5 min; 30 cycles of denaturation at 94°C for 30 s, annealing at 55°C for 45 s, and extension at 72°C for 90 s. Each PCR product was purified using a QIAquick PCR Purification Kit  and the refined PCR products were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit . Each DNA sample was mixed in the same amount and electrophoresis was performed on the pooled DNA sample. After emulsion-based clonal amplification of the DNA library samples, sequencing was performed using the emPCR Amplification 7020 Thermal cycler (Applied Biosystems) using the following program: initial denaturation step at 94°C for 4 min; 50 cycles of denaturation at 94°C for 270 s, annealing at 58°C for 45 s, and extension at 68°C for 30 s. Data analysis was performed using the CLcommunityTM v.3.46 software  and the EZBioCloud database.The composition of the gut microbiota was determined at ChunLab Inc. . PCR amplification was performed using fusion primers targeting the V3 to V4 regions of the Fecal SCFAs were measured using gas chromatography. Dried feces (10 mg) were mixed with 1 mL of methanol  and then shaken at 200 rpm for 90 min at 25°C. After the mixture was centrifuged at 10,000 rpm for 10 min at 25°C, the supernatants were filtered through a 0.22 μm syringe filter , and 2 μL of the filtrate was injected into YL 6100GC  to analyze the SCFA content in the colonic digest. Calibration curves were obtained using standard reagents .The results are presented as the mean ± standard error of the mean (SEM). The significance of the differences among the groups was determined using the SAS/PROC GLM software . The statistical significance according to dietary pattern was analyzed by one-way analysis of variance (ANOVA) with Duncan’s multiple range test. Statistical significance depending on the ADHD score was analyzed using Student’s t-test. Correlation analysis was used to analyze the relationship between the ADHD scores, intestinal microorganisms, and SCFAs according to the dietary patterns using the Pearson correlation coefficient.The dietary intake of the participants was investigated using FFQ. Factor analysis was performed to distinguish dietary patterns, and primary factors were separated through principal axis extraction and the varimax rotation method. As shown in p < 0.05) higher than those of the healthy dietary pattern. The ADHD group was identified using the criteria of Lee [The ADHD score of participants was investigated using the K-ARS, which was examined by the parents and class teachers, using a four-point scale and consisted of 18 questions, containing nine questions each related to the attention deficit and hyperactivity disorders, respectively. ADHD scores according to the four dietary patterns are as follows: healthy group, 10.04 ± 2.83; processed food group, 21.44 ± 4.74; fish and shellfish group, 13.73 ± 3.82; meat group, 15.56 ± 4.23. No significant difference in ADHD scores was noted among the healthy dietary pattern, meat dietary pattern, and fish dietary pattern, whereas those of processed food dietary pattern were remarkably  higher in the processed food group, and the ratio of Lactobacillus to Enterobacter strains was significantly (p < 0.05) lower in the processed food group than in the healthy group. The number of pathogenic bacteria, such as the Clostridium and Enterobacter strains, was significantly (p < 0.05) higher, whereas the ratio of Lactobacillus to Enterobacter strains was significantly (p < 0.05) lower in the ADHD group than in the control group.The results of qPCR are shown in Bifidobacterium, Faecalibacterium, and Ruminococcus strains, was significantly (p < 0.05) lower in the processed food group or meat group, whereas that of harmful bacteria, such as E. coli, was markedly (p < 0.05) higher in the processed food group than in the healthy group  higher in the ADHD group, whereas that of beneficial bacteria, such as the Bifidobacterium, Faecalibacterium, and Ruminococcus strains, was markedly (p < 0.05) lower in the ADHD group than in the control group  than that of the healthy group (p < 0.05) lower than that of the control group (The α-diversity of the processed food group was significantly lower (ol group .p < 0.05) correlated with the relative abundance of Escherichia strains, whereas it was negatively (p < 0.05) associated with the α-diversity and composition of Bifidobacterium, Faecalibacterium, and Ruminococcus strains. Furthermore, the ADHD score was significantly (p < 0.05) associated with increased composition of Escherichia strains, whereas markedly (p < 0.05) was associated with decreased α-diversity and the relative abundance of Bifidobacterium, Faecalibacterium, and Ruminococcus strains. These results showed that processed food dietary patterns may increase ADHD scores, disrupting the balance and diversity of the gut microbiota.The results of the correlation analysis for ADHD scores, dietary patterns, and gut microbiota composition are shown in p < 0.05) lower in the processed food group than in the healthy group, and the ADHD group showed significantly (p < 0.05) lower concentrations of fecal acetate and butyrate than those of the control group. The analysis of correlation among ADHD score, dietary patterns, and SCFA concentration in feces is shown in p < 0.05) correlated with the concentration of butyrate, and ADHD score was markedly (p < 0.05) associated with a decrease in the concentration of butyrate, compared to the control group.The fecal SCFAs of the participants were measured by gas chromatography. No significant decrease in the concentration of propionate was noted between the control and experimental groups . HoweverADHD Rating Scale (ARS)-VI is a tool developed to screen ADHD for children and adolescents aged 5 to 18 . The ARSIn this study, participants who consume probiotics, antibiotics, and health supplements were excluded from the survey, and we investigated the basic characteristics of participants, such as sex, age, BMI, parental smoking, parental income. The basic characteristics of participants did not significantly relate to the dietary patterns and ADHD scores. However, the covariates/confounders may not be sufficiently considered, resulting in bias of the research results.Alistipes strains, which are known to produce butyrate [Enterobacter strains [Dietary intake plays an important role in the modulation of gut microbiota and brain neurotransmission. Mental disorders are regulated by the function of the brain and an imbalance of the gut microbiota composition is likely to increase the incidence of mental disorders, such as ADHD . Some stbutyrate , and enh strains . In addi strains  reportedEnterobacter strains, whereas the ratio of Lactobacillus to Enterobacter strains was significantly lower than that of the healthy group. Moreover, the number of harmful bacteria, such as the Clostridium and Enterobacter strains, was considerably higher, whereas the ratio of Lactobacillus to Enterobacter strains was significantly lower in the ADHD group than in the control group. The ratio of Lactobacillus to Enterobacter strains is known as an index of a healthy intestine, and a high index indicates improvement in intestinal protection against opportunistic pathogens [The composition of the gut microbiota plays an important role in the maintenance of health by regulating metabolites, such as fecal SCFAs, which can protect the host from external pathogens and potentially harmful bacteria residing in the intestine . The resathogens . A varieathogens –40.Bifidobacterium, Faecalibacterium, and Ruminococcus strains, which are closely associated with ADHD, were significantly lower in the processed food group and ADHD group than in the control group. Beneficial bacteria, such as the Bifidobacterium strains, modulate the balance of gut microbiota composition and interfere with the toxicity of pathogenic strains and may have beneficial effects on psychological health [Faecalibacterium and Ruminococcus strains was markedly reduced in children with ADHD [Faecalibacterium strains can induce the production of SCFAs [Bifidobacterium strains [Heat map analysis of similar clusters analyzed using the CLcommunity program showed that the dietary intake was closely associated with the alteration of the gut microbiota composition, and these changes were further correlated with the ADHD score. The gut microbiota profile of the processed food and ADHD groups mostly belonged to distinct clusters, unlike that of the control group. The relative abundance of l health , 42. It ith ADHD . Moreoveof SCFAs , which c strains . This ca strains .The gut microbiota can synthesize neurotransmitters, such as dopamine , which cSalmonella, E. coli, and Clostridium strains [The concentration of fecal SCFAs was altered depending on the dietary pattern or ADHD score. No significant difference in propionate concentration of feces was noted between the control and experimental groups; however, the processed food group and the ADHD group showed a marked decrease in the concentration of acetate or butyrate compared to that in the control group. Fecal SCFAs, such as acetate and butyrate, can regulate intestinal homeostasis and have a direct effect on the gut microbiota composition . Several strains , 49. In  strains , and Dal strains  reported strains . The com strains . ThroughOur study several limitations should be considered. First, the number of samples is insufficient, and there is a limitation in that the evaluation of ADHD was conducted by their parent-assessment, not by a doctor’s diagnosis. The association between ADHD symptoms and dietary patterns can be biased to invalid as participants may underestimate ADHD symptoms. Second, there is a limitation in that bias may occur in the research results because covariates for the characteristics of the study participants are not sufficiently considered. Future studies need to increase the number of samples and analyse the stronger association between ADHD and dietary patterns using other methods of evaluating ADHD.Enterobacteria, E. coli, and Clostridium, was considerably higher, while that of beneficial bacteria, such as the Bifidobacterium and Ruminococcus strains, was remarkably lower in the processed food, meat, and ADHD groups than the control group. Therefore, the finding that the processed food and ADHD groups showed remarkably reduced α-diversity of gut microbiota and significantly reduced concentrations of acetic acid or butyric acid in the feces compared to the control group. Since our study has limitations according to the number of samples and the ADHD assessment method, it is necessary to conduct research through connection with hospitals in the future to derive more meaningful results.In summary, we analyzed the dietary patterns and ADHD scores of 40 school-age children using FFQ or K-ARS. The feces of the participants were collected to investigate the composition of the gut microbiota and SCFAs. The dietary patterns of candidates were divided into four groups: healthy, processed food, fish and shellfish, and meat. The participants were also separated into two groups according to their ADHD scores: control or ADHD group. The ADHD score of the processed food group was significantly higher than that of the healthy group. The abundance of harmful bacteria, such as


We used the Swanson, Nolan, and Pelham IV Scale (SNAP-IV), Conners’ Continuous Performance Test (Conners CPT), and Conners’ Continuous Auditory Test of Attention (CATA) to assess patients’ behavioral symptoms, visual attention, and auditory attention, respectively. Participants’ IFN-γ, IL-1B, IL-6, IL-10, IL-13, IL-17, MCP-1, and TNF-α plasma levels were assessed using multiplex assays. We found that the prevalence rates of atopic diseases  were similar between individuals with ADHD and those without ADHD. ADHD behavioral symptoms (SNAP-IV), CPT omission scores, and CATA detectability scores demonstrated significant differences between individuals with ADHD and those without ADHD, regardless of atopic diseases. However, plasma levels of cytokines  were negatively correlated with inattention symptoms. This study demonstrates a potential relationship between cytokine levels and neuropsychological function among patients with ADHD and atopic diseases.Since atopic disease and inflammatory cytokines are both involved in attention deficit hyperactivity disorder (ADHD), in this study, we examined the relationship among cytokine levels, neuropsychological function, and behavioral manifestations in patients with ADHD and atopic diseases. Participants were categorized into individuals with ADHD and atopic disease ( Attention-deficit/hyperactivity disorder (ADHD), one of the most well-known neuropsychiatric diseases in childhood, has an international prevalence rate of about 3–5% . ADHD isCytokines released during allergic inflammation may hinder the development of the neurotransmitter system and prefrontal cortex, thus influencing ADHD development ,11. HoweStudies comprehensively investigating the relationship among ADHD, atopic diseases, and cytokine levels are rare. In this study, we propose that immunological dysregulation plays a role in altering atopic diseases. Therefore, we recruited individuals with and without ADHD and with or without atopic diseases to compare the cytokine levels, behavioral symptoms, and neuropsychological function between these groups.The Institutional Review Board (IRB) at Chang Gung Memorial Hospital in Taiwan approved the research protocol for this study. We enrolled suitable patients with ADHD from the Outpatient Clinic of Child Psychiatry at Kaohsiung Chang Gung Children’s Hospital in Taiwan. Before the participants and their parents or guardians were entered into this study, the study protocols were explained to them. Their written informed consent was also acquired upon their agreement.Patients with ADHD were required to meet the following three criteria to be included: (a) a clinical diagnosis of ADHD made by a senior child psychiatrist through structured interviews utilizing the Diagnostic and Statistical Manual of Mental Disorders (DSM–5) ,23; (b) The control group consisted of children without an ADHD diagnosis or a history of major neuropsychiatric diseases. The control children (age between 6 and 16 years) were recruited from the communities surrounding Kaohsiung Chang Gung Memorial Hospital or from children who had upper respiratory tract infections (URI) but whose symptoms were currently remitted. Both patients with ADHD and control subjects were assessed by pediatricians in Chang Gung Children’s Hospital in Taiwan to confirm the diagnoses of atopic diseases . Their parents or guardians provided information about children’s health, home environmental exposures, and other relevant factors using a modified Chinese version of the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaires . All parParticipants’ blood tests were taken at about 8 o’clock in the morning. To check the levels of cytokines in plasma, a standard capture sandwich assay was developed with the Luminex Flowmetrix system . We coupled each capture antibody to a different bead set . Standards (recombinant cytokines) diluted in pooled blank plasma gathered from healthy grown-ups and test sera from patients or controls were analyzed using multiplex assays as previously reported, including IFN-γ, IL-1B, IL-6, IL-10, IL-13, IL-17, MCP-1, and TNF-α. Following a sequence, we incubated the beads with diluted standards or plasma and then with detection antibodies for 2 h each at room temperature, with biotin as a reporter for 1.5 h and with fluorescent dye-conjugated streptavidin–phycoerythrin for 30 min. We utilized a flow cytometer to estimate cytokine levels, which were then analyzed with Flowmetrix software, and then we mixed 50-μL samples with multiplexed antibody-conjugated beads prior to multichannel detection of the bead array.The Conners’ Continuous Performance Test (Conners CPT)  and ConnWe utilized the Swanson, Nolan, and Pelham IV Scale (SNAP-IV), which is a 26-item questionnaire for assessing ADHD symptoms and severity . The 26 p-values < 0.05 were considered statistically significant. We presented variables as either the mean (standard deviation) or frequency. Serum cytokine levels all exhibited significant levels of positive skewness, and arithmetic log transformations were used to create approximate normal distributions. To compare the ratios of atopic diseases between ADHD patients and controls, we applied either chi-square test or Fisher’s exact test, as appropriate. Independent t-test was used to examine the age difference between the ADHD group and the control group.The statistical software package SPSS, version 16.0 , was used to perform data analysis. Two-tailed Furthermore, one-way analysis of variance (ANOVA) was utilized to examine the age difference across the four groups of children , with ADHD without allergy , without ADHD but with allergy , and without ADHD or allergy ). We adopted multivariate analysis of covariance (MANCOVA) to compare clinical characteristics and cytokine levels among the aforementioned four groups. We set age and gender as covariates in the MANCOVA model. LSD post-hoc analyses were used for multiple comparison adjustments. Pearson’s correlation was performed to investigate relationships between cytokine levels, ADHD clinical symptoms, and neuropsychological performance (CPT3 and CATA).p > 0.05).For this study, we recruited 115 children with ADHD  and 72 control subjects . The characteristics of the ADHD and control groups are summarized in n = 41, with ADHD and atopic disease), ADHD+ Allergy− group , ADHD− Allergy+ group , or ADHD− Allergy− group . The characteristics of the four groups are listed in All participants were categorized into the ADHD+ Allergy+ group  revealed significant group differences, and we observed a similar distribution among the four groups. The post-hoc analyses showed that the children with ADHD (ADHD+ Allergy+ group and ADHD+ Allergy− group) demonstrated a higher severity of ADHD core symptoms than children without ADHD (ADHD− Allergy+ group and ADHD− Allergy− group), regardless of the presence of atopic diseases.The SNAP-IV scores of the four groups are illustrated in 3219 = 2.97, p = 0.033), CATA Omission scores , and CATA Detectability scores  indicated significant group differences. However, the CPT Commission scores , CPT Detectability scores , and CATA Commission scores  revealed no significant differences between groups. The post-hoc analyses revealed that omission scores of the CPT in the ADHD+ Allergy+ group were higher than those in the ADHD− Allergy+ and ADHD− Allergy− groups; meanwhile, the CPT Omission scores in the ADHD+ Allergy− group were higher than those in the ADHD− Allergy− group. The CPT Detectability scores in the ADHD+ Allergy− group were higher than those in the ADHD− Allergy+ group.The visual attention (measured by Conner’s CPT) and auditory attention (measured by CATA) of the four groups are shown in 3219 = 1.84, p = 0.142), IL-1B , IL-6 , IL-10 , IL-13 , IL-17 , MCP-1 , and TNF-α . Nevertheless, the post-hoc tests revealed that IFN-γ levels in the ADHD+ Allergy− group were lower than those in the ADHD− Allergy+ group. The IL-17 levels in the ADHD+ Allergy+ group were lower than those in the ADHD− Allergy− group. In addition, the TNF-α levels in the ADHD+ Allergy+ group were lower than those in the ADHD− Allergy+ group. The plasma levels of IL-1B, IL-6, IL-10, IL-13, and MCP-1 did not differ significantly among the four groups. Age had no significant effects on the aforementioned cytokine levels, but sex had significant effects on IFN-γ, IL-1B, and IL-17.The plasma levels of IFN-γ, IL-1B, IL-6, IL-10, IL-13, IL-17, MCP-1, and TNF-α among the four groups are displayed in p = 0.008) and CATA Commission scores . The levels of TNF-α  and IFN-γ  also showed negative correlation with SNAP-IV inattention scores. We found no significant correlation between levels of IL-1B, IL-6, IL-10, IL-13, and MCP-1, ADHD clinical symptoms, and neuropsychological tests.Among all participants, the plasma levels of IL-17 were negatively correlated to SNAP-IV inattention scores  to be comparable between ADHD patients and control groups. Three dimensions of ADHD symptoms, surveyed utilizing the SNAP-IV, showed significant differences between participants with ADHD and without ADHD, regardless of the presence of atopic diseases. Compared with their counterparts, children with ADHD are more likely to have allergic conjunctivitis, allergic rhinitis, asthma, and atopic dermatitis ,31. A meThe performance of neuropsychological tests  was less consistent with ADHD behavioral symptoms. However, higher CPT Omission scores and CATA Detectability scores were observed in children with ADHD (ADHD+ Allergy+ group and ADHD+ Allergy− group), regardless of the presence of atopic diseases. A previous study compared attention deficits among patients with allergic rhinitis (AR), patients with ADHD, and healthy controls . The resRegarding the plasma level of cytokines, IL-1B, IL-6, IL-10, IL-13, and MCP did not differ significantly between the four study groups. Meanwhile, cytokines TNF-α, IFN-γ, and IL-17 were negatively correlated with ADHD symptoms. Previous studies have also shown that TNF-αplasma levels in the ADHD group were significantly lower than those in the control group ,34. HoweThis study is the first to divide patients into four group  to investigate the possible effects on cytokines and neuropsychological tests. Another strength of this study includes adequate blood samples that measured eight biomarkers. Nevertheless, several limitations should be noted in this study. First, its sample size was small, with a notable disparity of the sample sizes across the four groups. This issue may reduce statistical power for studying behavioral measures and their correlates. Second, age and sex were not perfectly matched among the patients with or without ADHD or atopic diseases. Age and sex differences may be confounding factors of cytokines and psychological function. Third, atopic diseases were based on clinical diagnosis and the ISAAC, but IgE levels were not collected in this study. Cytokine levels other than plasma were not examined , and some essential type 2 cytokines, such as IL-4 and IL-5, were not examined in this study. Atopic diseases thus may have been misclassified. Fourth, the influence of atopic disease on cytokines or psychological function was not individually analyzed. Participants were mainly outpatients from a single hospital, and the age range was limited to ages 6 to 16 years.The findings in this study indicate that the prevalence rates of atopic diseases  are comparable between patients with ADHD and control groups. ADHD behavioral symptoms, CPT Omission scores, and CATA Detectability scores showed significant differences between individuals with ADHD and without ADHD, regardless of atopic diseases. However, the plasma levels of certain cytokines  were inversely correlated with ADHD inattention symptoms. This study demonstrates a potential relationship between allergic immune function and ADHD, and we suggest surveying for atopic diseases and immune function among children with attention deficits.


N = 20, 8–16 years). Math abilities were also measured by tasks indexing different mathematical competences. Numerosity performance and math scores were then compared to those obtained from an age-matched control group (N = 20). Bayesian statistics indicated no difference between ADHD and controls on numerosity perception, despite many of the symbolic math tasks being impaired in participants with ADHD. Moreover, the math deficits showed by the group with ADHD remained substantial even when numerosity thresholds were statistically regressed out. Overall, these results indicate that math difficulties in ADHD are unlikely to originate from an impaired visual number sense.There is ample evidence from literature and clinical practice indicating mathematical difficulties in individuals with ADHD, even when there is no concomitant diagnosis of developmental dyscalculia. What factors underlie these difficulties is still an open question. Research on dyscalculia and neurotypical development suggests visual perception of numerosity (the number sense) as a building block for math learning. Participants with lower numerosity estimation thresholds (higher precision) are often those with higher math capabilities. Strangely, the role of numerosity perception in math skills in ADHD has been neglected, leaving open the question whether math difficulties in ADHD also originate from a deficitary visual number sense. In the current study we psychophysically measured numerosity thresholds and accuracy in a sample of children/adolescents with ADHD, but not concomitant dyscalculia ( Attention deficit hyperactivity disorder (ADHD) is characterized by pervasive and severe symptoms of inattention, hyperactivity, and impulsivity having a direct negative impact on social, academic, or occupational functioning DSM5, . TogetheNumerosity perception refers to humans’  capacity to estimate numerical quantities (sets of elements) when serial counting is not allowed. Usually counting is prohibited through the rapid presentation of the visual stimuli (few milliseconds), and the classical tasks measuring this function are based on psychophysical procedures requiring the comparison of the relative numerosity of different groups of objects. Numerosity perception obeys Weber Law , a psychIndividuals with ADHD, even without comorbid dyscalculia, often report math difficulties. A relatively recent review systematized the data from 34 studies measuring math abilities in children and adults with ADHD . Out of Mathematics is not a monolithic construct, but composed of many different skills, including mental calculation, counting, fact retrieval, transcoding and many others. Although there is consensus in showing difficulties in learning mathematics in ADHD, the results are rather mixed in identifying which mathematics sub-components are impaired. To understand math difficulties in ADHD deeper, Overall, these (and many other) investigations  clearly indicate math difficulties in ADHD. However, the results are heterogeneous, the source of those difficulties is still unclear, and the potential role played by the numerosity system is completely neglected. The aim of the current study is to fill this gap. To this aim, we enrolled children/adolescents with ADHD but no concurrent dyscalculia nor any past or ongoing pharmacological treatments. Numerosity precision (thresholds: Weber fraction) and accuracy were psychophysically measured by a categorization task (see methods) and math abilities by means of an age-standardized paper-and-pencil battery designed for the diagnosis of dyscalculia. The performance on these tasks was compared to those obtained from an age-matched neurotypical sample.Given that the literature indicates math difficulties in individuals with ADHD, we expect lower performance. As previous evidence  showed rThis dataset is part of a larger project aimed to measure attention, time and numeracy in children and adolescents with neurodevelopmental disorders. Participants were tested by experienced psychologists and child neuropsychiatrists in a clinical setting at the Stella Maris Foundation Institute in Pisa . More specifically, participants were individually tested in a quiet, dimly lit room carefully avoiding environmental distraction factors. During the psychophysical assessment, participants were comfortably sitting on a chair in front of a desk where a monitor was placed. Visual stimuli were created with Psychophysics toolbox for Matlab  and dispd = 1.2) from a recent study employing similar methodologies with a sample of individuals with and without dyscalculia and an age range similar to the one considered here (t-test), an alpha of 0.05 and a required power of 0.95 provides an estimate of 20 individuals needed for each group. For this study we enrolled and test forty children/adolescents: 20 with a diagnosis of ADHD  and 20 with neurotypical development . Inclusion criteria for the group with ADHD were: clinical diagnosis of ADHD based on DSM-5, a total intelligence quotient (TIQ) evaluated with the Wechsler Intelligence Scale for Children-IV  is characterized by a numerosity perception deficit, and since there are not available studies on numerosity perception in ADHD, we calculated the required sample size extracting the effect size . Participants were then asked to count again but from the largest to the smallest number until he/she reached the time taken in the ascending counting task. The score was the total numbers stated correctly. (2) Numbers reading. Participants were asked to read aloud Arabic numbers (48 or 18 depending on age). Numbers were arranged in different lists each composed of 12 integer numbers . The score was the total numbers correctly read within 60 s. (3) Numbers writing. Participants were asked to transcribe numbers read out by the experimenter (three to six digits). The score was given by the number of digits correctly written. (4) Mental multiplications. Participants were asked to provide the result (within 3 secs) of simple multiplications  read by the examiner. Eighteen multiplications were administered to each participant and the score was the total correct answers provided. (5) Mental additions and subtractions. Participants were asked to provide the result (within 30 secs) of 9 addition and 9 subtractions  read by the examiner. The score was the total correct answers provided. 6) Choose the largest. Participants were given a sheet with 18 sets of three Arabic numbers (one to five digits) and asked, for each set, to mark the largest number. The score was a combination of both speed and accuracy.The tests were: (1) Numerosity perception was psychophysically measured with a categorization task , measuring Bayes Factors, the ratio of the likelihood of the alternative to the null hypothesis . A violation of normality was also detected in the correlational analysis between numerosity Wf and the aggregate math score (for both groups) as well as in the correlational analysis between numerical performance and ADHD symptomatology . This procedure was performed to make the two measures comparable between groups. Because of time constraints, one neurotypical participant completed 4 out of six math tasks, two neurotypical participants completed 5 out of six math tasks and one participant with ADHD did not complete any of the mathematical tasks  and male/female ratio (LBF = −0.47). Regarding non-verbal reasoning scores we found no sufficient evidence for a difference or no difference between groups .z-scores obtained in the six math tests (see z-scores (aggregate index) for controls was 0.4 (SD = 0.6) while for participants with ADHD it was –0.5 (SD = 0.7). Statistical analyses (t-test) confirmed the difference (LBF = 2.3) suggesting definitive evidence for a group difference. Given the heterogeneity of the math tests used, we also looked at each task separately. z-scores for each of the six math tests, divided by group (controls and ADHD). Overall, participants with ADHD performed worse compared to controls, across all the tasks , but some of the tasks were particularly impaired .The results so far suggest an intact numerosity system despite deficits in mathematical skills. Although this indicates a certain degree of independence of the two functions (numerosity and mathematics), we looked at inter-individual differences within the two groups. Separately for ADHD and controls, we correlated numerosity perception precision (Wf) with formal mathematical skills. To investigate this point further we asked whether the deficits shown by participants with ADHD in some of the mathematical tests were explained by inter-individual variability in numerosity estimation thresholds. To this aim we re-analysed the differences between groups in those mathematical tests where participants with ADHD showed a marked deficit and on the aggregate index, but this time entering numerosity estimation precision (Wf) as a covariate (ANCOVA). As a final check, we looked at the role of domain-general factors. To this aim, we repeated the ANCOVA analysis, but this time entering (simultaneously) numerosity thresholds (Wf), non-verbal reasoning scores , age and sex as covariates. Even when the contribution of all these factors was regressed out, the group differences on math abilities remained substantial .Within the sample with ADHD, we ran correlations between numerosity performance (thresholds and PSEs) math aggregate index, and both general  and specific clinical symptoms . For the CGAS test, which provided range scores, we transformed the ranges into categorical values reflecting the symptoms severity . The analyses revealed no meaningful correlations see .The current study is the first to measure visual numerosity perception in individuals with a diagnosis of ADHD. Results indicate similar thresholds (Weber fraction) and accuracy levels between neurotypical and age-matched participants with ADHD. Replicating previous studies, although the sample with ADHD recruited in the current study did not included individuals with dyscalculia, we found lower scores (compared to controls) along all the math tasks, with more severe difficulties in three out of six math tasks . Interestingly, when numerosity thresholds were regressed out, these impairments in math remained substantial. Overall, the results indicate that math difficulties in ADHD are unlikely to originate from an impaired visual numerosity system (number sense).Numerosity is considered a primary perceptual visual feature such as motion, color, orientation, and others . The numAt odds with the spared numerosity visual system, symbolic mathematical abilities were impaired in our sample of ADHD. This is largely in line with the literature. ADHD and dyscalculia are often associated and individuals with ADHD but not comorbid dyscalculia often report math difficulties . Our resAnother issue emerging from the current results is the null correlation, within the group with ADHD, between the clinical symptomatology severity and mathematical scores. This null result might indicate that the level of mathematical competence does not explain inter individual variance in the severity of ADHD clinical symptomatology. It should be noted, however, that the clinical tests used are mainly aimed at gathering information about the global child’s behavior and functioning in a variety of different life contexts. The “global” nature of the assessment, while clinically fundamental, may not be sufficiently specific and detailed in the measurement of the cognitive factors underlying mathematical abilities, such as executive functions (detailed below).If not numerosity, what is driving math underachievement in ADHD? The current study was specifically designed to address the role of the visual number sense and thus, with the current data, we cannot directly answer this question. However, the available literature on math abilities in ADHD suggests that domain-general functions—such as poor executive functioning (including working memory)—seems a good candidate. Executive functions refer to the processes required to monitor and control thought and action, allowing flexible responses to environment requests, and is composed of several skills such as inhibition, shifting, working memory and updating . There iGiven the behavioral nature of the current work, we cannot say anything definitive about the neural structures underlying the observed pattern of results, however, these are generally in line with a previously suggested idea that the frontal cortex could be relatively more impaired compared to the parietal cortex in individuals with ADHD . As mentTo summarize, by confirming math difficulties in ADHD but showing a spared visual numerosity system, our results indicate a different etiology underlying the arithmetic difficulties encountered in dyscalculia and those encountered by individuals with ADHD . These results have implications for the design of math interventions in the ADHD population, encouraging the targeting of domain-general functions.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of the Meyer’s Hospital . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.GA, MB, and FT: conceptualization. GA, MB, GM, and FT: methodology, resources, and writing—review and editing. GA: software, writing—original draft preparation, and visualization. GA and FT: formal analysis. GA, GM, and FT supervision. MB, GM, and FT: project administration. All authors read and agreed to the published version of the manuscript.


The higher heating value—HHV (19.39 MJ kg−1 at 10% humidity) and the lower heating value—LHV (18.17 MJ kg−1) of sage waste biomass indicate that this plant material is highly suitable for heat generation. The addition of 20% rye bran decreased HHV by 2.07% and LHV by 2.67%.The aim of the present study was to evaluate the effect of rye bran addition on the pelleting process of sage waste biomass, and the quality and energetic properties of fuel pellets. The pelleting process was conducted on an SS-4 test stand equipped with a P-300 pelletizer with flat die roller compactors. The addition of 20% rye bran reduced the pelletizer’s power/energy consumption from 3.75 kW/107 kWh t The depletion of fossil fuels necessitates the search for more innovative sources of energy. Renewable energy sources, including wind, solar radiation, and geothermal energy, are gaining increasing popularity. However, the use of biomass as a source of renewable energy has recently attracted the greatest attention. Various forms of biomass  have been used as an energy source for many years. The suitability of biomass for energy generation has been confirmed by numerous research studies.https://www.eia.gov [According to the International Renewable Energy Agency , bioener.eia.gov , accesseCereal straw, mainly wheat straw, is widely used as feedstock for bioenergy generation. The suitability of compacted cereal straw for energy purposes was investigated by Azócar et al. , ObidzińAccording to Kobus et al.  and KumaHerbal waste can be converted to liquid fuel  and biogMoringa oleifera leaf meal, which is widely available in many tropical countries, is also a good source of antioxidant compounds such as ascorbic acid, flavonoids, phenolics, and carotenoids. Kongmun et al. [Mentha arvensis) enhanced ileal protein and amino acid digestibility and improved feed efficiency in weaned piglets [Most herbal waste biomass is processed into feed additives. Kisworo et al.  analyzedn et al.  reportedn et al. . Menthol piglets , whereas piglets .Cellulomonas favigena and Streptomyces viridosporus bacterial inoculants on the compostability of post-extraction lavender waste and found that lavender waste enhanced the composting process by extending the thermophilic phase, accelerating OM degradation, and increasing the viable counts of beneficial microorganisms. However, adverse effects were also observed.Herbal waste can also be managed by composting ,39,40. AIn Poland, herbal plants are grown on an area of 25,000 ha . Around Sage is a widely cultivated herbal plant, and waste biomass is produced mainly during harvest. Sage has antimicrobial and antioxidant properties, and it used in the production of pharmaceuticals and cosmetics ,25,26. SCoppice willow and Virginia mallow biomass, and Stasiak et al. [In recent years, waste biomass has become a popular feedstock for heat generation. Gill et al.  studied k et al.  analyzedk et al.  pelletizk et al.  analyzedBran is composed of the pericarp, the aleurone layer, and the seed coat, with an admixture of ground germ and endosperm . Bran caAccording to Kamal-Eldin et al. , rye braDue to its high content of dietary fiber, rye bran is used in the production of dietary supplements to stimulate peristalsis and decrease the energy density of the diet (by increasing and prolonging satiety) .Rye bran is also an effective binder, and it is applied in granulation and compaction processes to decrease energy consumption and improve the quality of pelleted materials ,66,67.The aim of the present study was to determine the effect of rye bran addition (10% to 20%) on the pelletization of sage waste biomass and the quality of the obtained pellets (including their energetic properties). Ideally, the biofuel production process should be energy-efficient; therefore, an attempt was made to analyze the suitability of an easily available additive, i.e., food processing waste, for energy generation. The results of the study can be used to optimize the proportions of rye bran in pellets made from sage waste biomass, maintain desirable physical  and energetic properties of pellets, and reduce energy consumption during pellet production.Sage waste biomass comprising shredded stem and leaf residues a, and ryThe moisture content of the tested raw materials and mixtures was determined using the RADWAG MA50/1. R moisture analyzer, according to standard . MoisturBiomass was shredded with a Bąk-H-8111 flail shredder equipped with a set of sieves with ϕ 4mm mesh size. The particle size distribution of the tested raw materials was determined using a MULTISERW-MOREK laboratory sieve, according to standard  and a prThe higher heating value (HHV) and the lower heating value (LHV) of the tested raw materials was determined according to standard  and a prA pellet that was previously produced with a hand press was placed in the bomb calorimeter, and the ends of the ignition wire were wrapped around the electrodes. The calorimeter was closed, inflated, and pressurized with oxygen. The device was filled with water with a temperature that was 1 K lower than the temperature of water in the calorimeter jacket. The wires were properly attached and connected to the computer controlling the calorimeter, and the test was conducted.saQ of the tested pellet was determined in the laboratory and used to calculate the LHV by entering the content of hydrogen, moisture, ash, and sulfur into the calorimeter software. Hydrogen, ash, and sulfur content was determined in an elemental composition analysis of the tested raw materials, which is not presented in this paper. Hydrogen content was determined at 6.0% in rye bran and 4.5% in sage straw; ash content was determined at 3.78% in rye bran and 6.1% in sage straw; and sulfur content was determined at 0.33% in rye bran and 0.1% in sage straw. The calculations were performed using the formula provided in the instruction manual of the Precyzja-Bit KL 10Mn calorimeter (1):The HHV W—moisture content of the sample (%),aH—hydrogen content of the sample (%),24.43—coefficient that accounts for the heat of water vaporization at 25 °C in pellets with 1% water content,8.94—coefficient that accounts for the stoichiometry of the hydrogen combustion reaction (quantitative changes).The total content of organic carbon was determined by high-temperature decomposition with infrared detection in the Schimadzu TOC-L analyzer. A weighed sample of ground waste biomass was placed in a ceramic vessel and burned in the device. Total nitrogen content was determined by the Kjeldahl method in a Vapodest 50S device. A ground waste biomass sample of around 0.5 g was mineralized in concentrated sulfuric acid, and the resulting solution was distilled and titrated in the Vapodest 50S device.Pellets were produced in an SS-4 test stand equipped with a P-300 pelletizer with flat die roller compactors, according to a previously described methodology ,13. The −1 and a rotational speed of 280 rpm. Die openings had a diameter of 6 mm and a length of 28 mm.The bulk density of the sage straw and rye bran mixture in the pellet mill was determined with a 0.4 mm gap between the roller compactor and the die, at a mass flow rate of approx. 35 kg hThe influence of rye bran addition  to sage straw on the energy consumption of the pellet mill, physical density and bulk density, and the pellet durability index (PDI) was determined in the SS-4 test stand.Physical density was determined by measuring the length of 10 cylindrical-shaped pellets with a caliper to the nearest 0.5 mm and by weighing the pellets on the OHAUS AX224M analytical balance. Physical density was calculated as the mass/volume ratio of 10 randomly selected pellets with the use of Equation (2). To determine the volume of pellets with a diameter of 6 mm, pellet length was measured with a caliper to the nearest 0.05 mm. Mass was determined by weighing pellets on the OHAUS AX224M analytical scale. The diameter of die openings was equal to pellet diameter (6 mm). Physical density was calculated according to formula (2):gρ—physical density of pellets (kg m−3),m—pellet mass (kg),gV—pellet volume (m3).The bulk density of pellets was determined according to standard  by filliThe PDI was calculated according to standard . During The particle size distribution of sage waste biomass is presented in −1 when biomass milling size was reduced from coarse to fine. Stelte et al. [Numerous research studies have shown that the particle size distribution of compacted materials is one of the most important parameters which influence the physical properties of pellets and the course of the pelleting process. Harun and Afzal  reportede et al.  observede et al. , the dene et al.  who foun−1, and the LHV of sage straw (with 10.1% moisture content) was 18.169 MJ kg−1. An increase in moisture content decreases both LHV and HHV, as demonstrated by the results in The HHV and the LHV of sage straw and rye bran are presented in −1, and the LHV of rye bran was 15.124 MJ kg−1.The energy parameters of rye bran were determined in samples with an average moisture content of 13.2%. The results are presented in −1 and 16.93 MJ kg−1 DM, respectively. Miranda et al. [−1. In a study by Gageanu et al. [−1. According to Nakomcic-Smaragdakis et al. [−1.Similar values of HHV and LHV were reported in the literature for other types of biomass. In the work of Jasinskas et al. , the lowa et al.  determinu et al. , the lows et al. , apple pHHV of sage waste biomass:Based on the results presented in LHV of sage waste biomass:HHV of rye bran:LHV of sage waste biomass:ssQ, sbQ—HHV of sage straw and rye bran (MJ kg−1),isQ, ibQ—LHV of sage straw and rye bran (MJ kg−1),sw, bw—moisture content of sage straw and rye bran (%).The moisture content of the analyzed material is entered into the program to calculate the higher heating value (HHV) and the lower heating value (LHV). The program calculates HHV and LHV values for the determined moisture content and for dry weight with 0% moisture content. Therefore, the equations for calculating HHV and LHV are derived for the analyzed moisture content and for 0% moisture content.Various formulas for calculating HHV/LHV have been proposed in the literature, depending on the parameters of the analyzed materials. The formulas applied in this study are based only on the moisture content of the examined materials.The results presented in Formulas (3) to (6) were also used to calculate the HHV and LHV of sage straw pellets with different proportions of rye bran (15% moisture content). The results are presented in −1 (approx. 1%).The results presented in −1 for pure pine sawdust [−1 for crushed pine cones [Binder addition exerted similar effects on the HHV and LHV of the obtained pellets in other studies ,84,85. G sawdust  to 18.32ne cones . Similar−1) to 24.92 (MJ kg−1) for corn cobs, and from 14.61 (MJ kg−1) to 25.35 (MJ kg−1) for wheat dust.El-Sayed and Khairy  pelletizThe nitrogen and total carbon content of rye bran and sage straw were determined in the last test, and the results are presented in For rye bran:Hydrogen—6.0 ± 0.53 ;Sulfur—0.33 ± 0.01 ;Ash—3.78 ± 0.06 .The average total carbon content was 46.32% in rye bran and 34.92% in sage waste biomass, whereas the average nitrogen content was 2.45% in rye bran and 1.72% in sage. It should be noted that total carbon and nitrogen content was significantly higher in rye bran than in sage waste biomass.The values noted in rye bran were similar to those reported by Čajová Kantová et al.  who anal−1 (0% rye bran content) to 3.19 kW/91 kWh t−1.The effect of rye bran addition on the pelletization process of sage waste biomass and the quality (properties) of the obtained pellets is summarized in The results of energy consumption measurements are presented in bz on the energy consumption of the pellet mill gN during the compaction of the sage straw and rye bran mixture in a flat die pelletizer was determined using Equation (7):The influence of rye bran bz—rye bran content of the mixture (%).−1 to 39.09 Wh kg−1. Garcia et al. [−1, whereas an increase in the moisture content of the blend increased specific energy consumption.The obtained results indicate that rye bran acts as a binder. The binding capacity of rye bran increased with a rise in rye bran content (the additive absorbed water present in the mixture). Rye bran had a lubricating effect on the surface of die openings in the pelletizer, thus reducing the resistance of the material passing through the openings and, consequently, decreasing the mill’s energy consumption . In othea et al.  pelletiza et al.  investiga et al.  found thHigher rye bran content induced a minor decrease in pellet density. Despite the above, an increase in rye bran content improved PDI values, thus decreasing energy consumption during pelletization and improving pelletization effectiveness.−3 in pellets containing 0% rye bran to 1145.05 kg m−3 in pellets with 20% rye bran addition. The influence of rye bran content bz on the density ρ of the resulting pellets was calculated using Equation (8):An analysis of pellet density revealed that rye bran addition significantly had a significant effect on the physical density of pellets. The results of measurements are presented in −3 in pellets without potatoes and 1092.3 kg m−3 in pellets with potatoes) and significantly improved the mechanical strength of the pellets (4.39% in pellets without potatoes and 0.71% in pellets with potatoes).In other studies, the addition of a binder to compacted biomass also improved the physical properties of pellets. Emadi et al.  producedbz on the bulk density bρ of the resulting pellets was calculated using Equation (9):Rye bran addition caused a similar improvement in the bulk density of pellets . Higher −3 in pellets composed solely of sage straw, and it decreased to 611.54 kg m−3 in pellets containing 10% rye bran and to 547.16 kg m−3 in pellets containing 20% rye bran. Bulk density was nearly 13% lower in pellets with 20% rye bran addition than in pellets made from sage straw alone. Bulk density decreased with a rise in rye bran content. The bulk density of pellets composed solely of sage straw was comparable to that of oak sawdust pellets (624.5 kg m−3), and the bulk density of pellets containing 15% rye bran was similar to pellets made from willow harvested over a one-year cycle (583.22 kg m−3) [Bulk density was highest at 626.24 kg m kg m−3) .2 was much lower when a linear trendline was used. The rapid decrease in PDI values in pellets containing more than 15% of rye bran could be attributed to a decrease in physical density. Pellets lose their cohesiveness when a certain physical density limit is exceeded.The PDI was highest at 98.14% in pellets with 15% rye bran addition . PDI valbz on the PDI of pellets produced from sage waste biomass in a flat die pelletizer was determined using Equation (10):The influence of rye bran −3) in briquettes manufactured with 10% addition of xanthan . A combination involving 5% addition of xanthan  was most durable, with a mass loss of only 3.9%. However, binders exerted a different effect on the properties of biofuel pellets manufactured from yak manure by Liu et al. [Carrillo-Parra et al.  producedu et al. . The tesIn the present study, pellets with 15% rye bran content were characterized by a high PDI 98.14%) which was comparable to the values noted by Kraszkiewicz et al. [.14% whicIn recent years, various waste biomass materials, including agricultural waste, garden waste, food industry waste, and herbal waste, have been used for heat generation. Sage straw, as a by-product of sage production, is one type of herbal waste biomass that can be used for this purpose.In this study, sage straw with the addition of rye bran  was pelletized in the SS-4 test stand using a P-300 pelletizer with flat die roller compactors. The study demonstrated that rye bran addition had a significant effect on the pelletizer’s energy consumption, the quality of the obtained pellets , and the HHV and LHV of pellets.An increase in rye bran content decreased the pelletizer’s energy consumption and increased PDI values, but it exerted a negative impact on pellet quality  and decreased the HHV and LHV of pellets. Pellets containing sage straw only were characterized by the highest LHV (17.023), but had a low PDI (97.17%); therefore, the addition of rye bran can improve the quality of the resulting product. Even small quantities of rye bran increase the mechanical strength of pellets and reduce the pelletizer’s energy consumption.


Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders. Its onset is typically in childhood, but it persists very frequently into adulthood. Although the deleterious impact of its core and accessory symptoms on all the individual's functioning areas is well-known, ADHD can remain underdiagnosed in adults also because of the high rate of co-occurrence with other psychiatric disorders . AccessoThe present research topic aimed to deepen ADHD issues in adults considering the influence of other psychiatric disorders that commonly lead to complex psychopathology and clinical presentation.As editors of this Research Topic, it was our pleasure to review several papers. Among these, the following four articles responded to the topic aim, encompassing different points of view, ranging from neuroimaging to patients' subjective perception of ADHD core and accessory symptoms.Gerhardt et al. specifically compared ADHD impulsivity with that of alcohol use disorder. The authors examined the subprocesses that constitute the response inhibition  by scanning cerebral regions while patients and controls were performing a hybrid response inhibition task. Results confirm that impulsivity is a common phenotype in these two diagnoses, and also shed light on different functional impairments underpinning this characteristic in those two clinical conditions.As regards symptoms overlapping, in their thorough fMRI study, Van Rooij et al. focused on morphological brain markers that may distinguish adult patients between those who present ADHD alone and those who are also affected by reward-related comorbidities . They conducted a comprehensive neuroimaging investigation, finding no significant difference in cortical thickness in the medial, dorsal, and frontal brain areas. These findings suggest that brain morphometry cannot help disentangling the complex relationship between ADHD and these comorbidities at a biological level.On the other hand, Hertz et al. is one of the few considering a variety of sexual behaviors among adult patients with ADHD in comparison with non-ADHD controls. Furthermore, the authors explored the relationship between ADHD symptoms and sexual behaviors  as well as sexual dysfunctions. Attention deficit and hyperactivity disorder patients of both sexes reported higher rates of sub-clinical hypersexual behaviors than controls, but unexpectedly no differences emerged concerning risky sexual behaviors and sexual dysfunctions when patients and controls were compared. A positive correlation has been found between impulsivity, emotional dysregulation, oppositional symptoms severity and hypersexuality, risk-taking behavior, and sexual dysfunctions.The study by Ginapp et al., reporting about the lived experience of adults with ADHD. Thanks to their systematic literature review focused on the patients' subjective perceptions of the disorder, readers can realize the actual impact of ADHD core and accessory symptoms on daily life of people affected, even discovering some unexpected positive effects of impulsivity  and attention deficit . The article yields a clear and comprehensive picture of the subjective experience of the person with ADHD also reporting about the huge effect of diagnosis and treatment.Finally, we published an original and outstanding review conducted by All authors contributed to manuscript revision, read, 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.