Mapper, to analyze the brain functional connectivity data from ADHD patients.Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder of childhood, affecting at least 5% of school-age children worldwide . ChildreOwing to the limited understanding of the biological underpinnings of mental disorders, ADHD is currently diagnosed using the criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM)  rather tWith this background, the interest in neurobiological markers of ADHD has grown substantially in recent years –14. In pMapper, is widely used in analyzing high dimensional behavioral , can be obtained as illustrated in Fig 1, where i represents the subject index, j represents the j-th elements of Ti, and the vector Ti is the i-th row vector of D = [Dij].Briefly, for the construction of the functional network, we used the extracted time courses from the Athena preprocessed data. A detailed description of the preprocessing steps can be found in the Supporting Information as well as on the Athena website. The filtered time course files, ADHD200_AAL_TCs_filtfix.tar.gz, can be downloaded from the ADHD-200 Preprocessed Data website. The functional network of each subject  measures correlation coefficients between two vectors: X and Y.In this study, the topological data analysis was used to extract a geometric shape from the relationships among subjects by using a new technology named “partial clustering”. Initially, analysis ,34 was iFig C in S1 File). In the resulting graph, each node is a subset of subjects, and edges connect similar nodes. The color of each node encodes the value of the filter function averaged across all the data points to the node, with blue representing a low value and red denoting a large value.The essential role of the filter function is to collapse high-dimensional functional network data to a single data point and to capture a neurobiologically meaningful characteristic of the data . In the First, group differences in the values of the filter function, which represent the magnitude of the disease component, were examined by a one-way analysis of variance (ANOVA). Second, Pearson’s correlation coefficients between the values of the filter function and clinical phenotypic variables were evaluated to find the significant relationships between these measures. Third, for the value of the filter function, we conducted analysis of the receiver operating characteristics including the estimation of sensitivity and specificity. Fourth, the correlation analysis was conducted to reveal a relationship between a psychiatric comorbidity and resulting topology. For this analysis, we calculated Pearson’s correlation coefficients between the ratios of the subject with psychiatric comorbidity in each node in the resulting topology and the node index, where the node index represents the node number with lower (higher) index indicates a subset of subjects having a lower (higher) value of the filter function.Table 1). There was no significant difference in age between the TDC and ADHD group, but the proportion of males was higher in the ADHD sample compared to the TDC sample. In the dataset, several subjects did not have scores from clinical measures and were excluded from the correlation analysis (Table 2). Scores of the ADHD index were significantly lower for PU than for NYU participants , which likely reflect differences between the ADHD Rating Scale IV (PU) and the Conners’ Parent Rating Scale-Revised, Long Version (NYU). In addition, we computed the ratio of comorbidity in patients with ADHD for the NYU and PU datasets. In the NYU dataset, 36% (42 of 118) of patients with ADHD had the following comorbid psychiatric symptoms: anxiety disorder (15 patients), depressive disorder (8 patients), learning disorder , ODD (6 patients), and other disorders (7 patients). In the PU dataset, 53% (41 of 78) of patients with ADHD had the following comorbid psychiatric symptoms: ODD (25 patients), LD (7 patients), tics (6 patients), conduct disorder (3 patients). Due to this substantial difference in clinical characteristics of each dataset, analyses were conducted separately for the NYU and PU datasets.Some differences existed in the clinical characteristics between participants from NYU and PU  when evaluating the functional connectivity (r = 0.9940 for the NYU and r = 0.9909 for the PU dataset). A one-way ANOVA found significant group differences in the values of the filter function . We have not found any significant confounding effects of the phenotypic information, such as age, gender or medication status, to the group differences in the magnitude of the disease component (Table C in S1 File). Also, the value of the filter function, which measures the magnitude of the disease component, has excellent sensitivities and specificities (>96%) for the diagnosis of the children with ADHD at a cut-off score of 12 (11) for the NYU (PU) dataset (Table D in S1 File).The filter function successfully measured a magnitude of the disease component; the subjects with smaller values of the filter function, which represented the smaller magnitude of the disease component, were mostly in the TDC group while the subjects with larger values of the filter function were mostly patients with ADHD. Mapper was applied to the functional neuroimaging data and the chain-like graph was obtained as a result . The blue-colored nodes contained mostly normal subjects, whereas red-colored nodes contained patients with ADHD who generally had large deviation from the functional network of the healthy subjects. The illustrations of the number of subjects and the occupation ratio of group members are presented in Fig 3B and 3C.Topological data analysis using Fig 4) and quantitative correlation analyses (Table 2) were performed to find the hidden relationship between the Mapper results and clinical phenotypic variables. We inspected each node and computed the average value in each node. First, the visualization of the symptom severity as a function of node index revealed that the blue color nodes, whose index numbers ranged from 1 to 10, had significantly lower symptom severity than those of the red color nodes, whose index numbers ranged from 40 to 49 . Significant statistical differences in the ADHD index, hyperactivity/impulsivity, and inattention scores between the blue and red color nodes were confirmed (p < 0.0005). Especially the symptom severity, represented by the ADHD index score from the PU dataset, showed positive correlations with the values of the filter function . However, for the NYU sample, the ADHD index did not show significant correlations with the value of the filter function. Second, the visualization of the intelligence scale as function of node index showed the decreasing trend across blue color nodes, which index number ranges from 1 to 19, and the constant trend across red color nodes, which index number ranges from 20 to 49 . In the TDC group, we revealed that the significant negative correlations between the intelligence scales and the values of the filter function as described in Table 2. We compared 10 TDC subjects with the highest value of the filter function with the lowest values regarding demographic factors and ADHD symptom severity. The result shows that TDC subjects with higher values of the filter function consist of a younger age group (NYU dataset), contain more females (NYU dataset), have significantly lower IQ (NYU and PU datasets), and more severe hyperactivity/impulsivity (NYU dataset) compared to those with lower values of the filter function (Table 4). Finally, the correlation analyses found a significant positive relationship between the ratio of subjects with psychiatric comorbidity in each node and the node index for the NYU  and PU  datasets, respectively.Since one important goal of topological data analysis is to obtain knowledge about the data followed by quantitative analysis, qualitative graphical . Originally, we expected that Mapper would yield a branch-shaped graph with two progressive arms; each arm differentiating the inattentive subtype of ADHD from the combined subtype, similar to two subtypes of breast cancer that were detected in a previous study [Mapper result is presented as a long gradual progression , showing ADHD symptoms as dimensional (or quantitative) traits that form a continuum with the normal state.The partial clustering methods through topological data analysis produced an easily recognizable chain-like graph , significant negative correlations between the magnitude of the disease component  and the IQ score were found in the TDC group but not in the ADHD group. Our results agree with recent studies showing that brain structure  or small-world network parameters were associated with IQ for controls, but not for ADHD [According to our results . However, significant correlations were not found in the NYU dataset . Although the reasons for the different results from each institution are unclear at present, one possible explanation would be the difference in phenotypic characterization of the study samples at both sites. For example, ADHD symptoms were measured using different rating scales in each institution . Therefore, further studies to replicate the PU findings in different datasets are needed to reach a definitive conclusion.In addition, a significant positive correlation between the magnitude of the disease component represented by the value of the filter function and ADHD index score was found in the PU dataset . Nonetheless, the comparison of the NYU dataset with the independent PU dataset produced very similar results, except for the one result regarding the association between the value of the filter function and ADHD symptoms. Thus, the methodologies we propose may be considered as relatively unaffected by these sources of variation and bias caused by differences between study samples. Second, two groups were successfully identified on the basis of brain imaging data, such as the magnitude of the disease component, but phenotypic information such as gender, age, and medication status were not included in the topological data analysis. The demographic information may also contribute greatly to the disease classification, as shown in the competition results from the ADHD-200 dataset, which shows that diagnosis based on demographic variables outperforms imaging-based diagnostic prediction. Third, additional interaction effects of disease duration and medication may further impact the neurobiological substrate in specific ways [The results mentioned above should be interpreted with caution with the following limitations in mind. First, the ADHD-200 data have site-specific differences in behavioral measurement, imaging data acquisition, scanner quality and protocols, and subject populations from the site contributing to the data. To overcome these problems, we analyzed the NYU and PU dataset separately, and mainly presented the results from the NYU site for this study . Mapper for NYU dataset (Table B)Number of subjects in each group (TDC and ADHD) for each bin of the output graph of . The effects of the phenotypic information to the magnitude of the disease component (Table C). The summary of receiver operating characteristic (ROC) analysis using the value of the filter function (Table D). The decomposition of the original functional connectivity vector Ti into the Normal component, which is the linear models fit Ti with (A) small and (B) large disease component were visualized . The areas under the receiver operating characteristics (ROC) curves for the value of the filter function were illustrated for (A) the NYU and (B) PU dataset, respectively . Schematic diagram of topological data analysis using Mapper. (A) The data is sampled from a noisy Y-shape point cloud in the two-dimensional space, and the filter function is f = y. We divided the range of the filter into 5 intervals and a 50% overlap. (B) For each interval, we compute the clustering of the points lying within the domain of the filter restricted to the interval. Distributions of the distances from single linkage dendrogram in each filter bin. For example, distance distributions for 1st and 9th filter bin were presented. The summation of frequencies appeared after zero bins is the number of clusters, (C) Finally, we have the simplicial complex by connecting the clusters whenever they have non-empty intersection. The color of vertices represents the average filter value . Sample application of Mapper to the Y-shape noisy point cloud. In this example illustration, 5 intervals with 20–80% overlaps and 10 intervals with 80% overlap are the appropriate choose of the input parameters of Mapper . Sample application of Mapper to O-shape noisy point cloud data. In this example illustration, 5 intervals with 50–80% overlaps, 10 intervals with 50–80% overlaps, and 15 intervals with 80% overlap are the appropriate choose of the input parameters of Mapper . Visualization of the clinical phenotype data as a function of the node index in the PU data: (A) The average symptom severity in each bin of graph; (B) The average intelligence scores in each bin of graph .Mean and standard deviations (SD) of the symptom severity and intelligence scale in NYU dataset and PU dataset (DOCX)Click here for additional data file.


Previous studies suggest that British children of South Asian origin are less active and more sedentary than White British children. However, little is known about the behaviours underlying low activity levels, nor the familial contexts of active and sedentary behaviours in these groups. Our aim was to test hypotheses about differences between British Pakistani and White British girls using accelerometry and self-reports of key active and sedentary behaviours, and to obtain an understanding of factors affecting these behaviours using parental interviews.Participants were 145 girls (70 White British and 75 British Pakistani) aged 9–11 years and parents of 19 of the girls. Accelerometry data were collected over 4 days and girls provided 24-hour physical activity interviews on 3 of these days. Multilevel linear regression models and generalised linear mixed models tested for ethnic differences in activity, sedentary time, and behaviours. Semi-structured interviews were conducted with parents.Compared to White British girls, British Pakistani girls accumulated 102  fewer counts per minute and 14 minutes  less time in moderate to vigorous physical activity per day. British Pakistani girls spent more time  sedentary. Fewer British Pakistani than White British girls reported participation in organised sports and exercise  or in outdoor play . Fewer British Pakistani girls travelled actively to school . There was no significant difference in reported screen time . Parental interviews suggested that structural constraints (e.g. busy family schedules) and parental concerns about safety were important influences on activity levels.British Pakistani girls were less active than White British girls and were less likely to participate in key active behaviours. Sedentary time was higher in British Pakistani girls but reported screen-time did not differ, suggesting that British Pakistani girls engaged more than White British girls in other sedentary behaviours. Interviews highlighted some differences between the groups in structural constraints on activity, as well as many shared constraints. Adults of South Asian origin living in the UK, Canada and the US have a higher risk of type 2 diabetes and cardiovascular disease than those of European origin-3 and diMuch less is known about the differences in behaviour that underlie these differences in activity levels. In children, more participation in organised sports or exercise, in outdoor play and in active travel to school have been shown to contribute to objectively assessed total physical activity and to the percentage of time spent in moderate to vigorous activity (MVPA)-14. DescScreen-time is the most commonly researched sedentary behaviour and it appears to be more strongly associated with risk of obesity and cardiometabolic disease than is objectively assessed sedentary time. While sAs in other ethnic groups, girls of South Asian origin are less active than boys. AlthougWe therefore conducted a focused and detailed mixed methods study of activity and sedentary time comparing British Pakistani girls, who might be expected to be a particularly inactive South Asian group, with White British girls. We set out to compare objective measures of activity, to characterise differences between this group and White British girls in key active and sedentary behaviours using optimised methods of self-report, and to interview parents in an effort to understand the family context of these behaviours. We tested predictions that British Pakistani girls would be less active and more sedentary, as assessed by accelerometer, than White British girls and that British Pakistani girls would engage less in active behaviours  but expected no difference in screen-time compared with White British girls. We considered it important to provide a more complete picture of activity levels and behaviours underlying activity as it is only when behaviours and their contexts are understood that effective intervention studies can be designed.In order to recruit year 5 (aged 9–10 years) and year 6 (aged 10–11 years) girls to the study, 8 primary schools in a large economically deprived conurbation in North East England known to have a relatively high proportion of British Pakistani children were approached.Parents of participating girls were sent a letter asking if they would be willing to participate in an interview. Informed written parental consent and verbal child assent to participate was provided by all participants. The research design and protocol received ethical approval from Durham University ethics committee. Data were collected between June 2009 and December 2010. No data were collected during Ramadan (21 August - 19 September 2009 and 11 August – 9 September 2010).Parents were asked to complete a general socio-demographic questionnaire. Ethnicity was based on parentally defined ethnicity for parents if provided, otherwise geographic origin of parents and grandparents, otherwise parentally defined ethnicity of child, otherwise child-defined geographic origin of parents and grandparents.http://www.mrc-epid.cam.ac.uk/research/resources/materials-transfer-disclaimer/physical-activity-downloads). Periods of twenty minutes or more of continuous zero activity counts, which were considered to indicate non-wear, were removed that’s the only time they want to stay in. WB Mother.Every night she’ll come home and she’s out playing, playing football, out on the scooter, on her bike, she’s always out, she always comes home sweating and red. WB Mother.She comes back from mosque at 6.30, has her dinner, then she plays in the back, on the bike, roller-skates. Her cousins play out too, for one hour, then she comes back in. BP Mother.Some outdoor play took place with other members of the family, especially in parks, a resource utilised by families of both ethnicities:We like Albert Park. I’ve always took her, from when she was a baby, so we go there quite a lot. WB Mother.If it’s summer time then the oldest daughters will take the other two girls to the park and spend an hour or two there. BP Mother.I play cricket with the kids. I try to…spend as much time with them as I can, to encourage these sorts of things. …They come over to me and say “Dad, let’s go play football…” BP FatherParental concerns about traffic or about personal safety limited opportunities for unsupervised outdoor play for some children, particularly those living in city-centre terraced (row) housing without gardens:She plays out more at her Dad’s as round here that road can be quite busy, but round near her Dad’s is more quiet and she has a lot of friends there. WB Mother.Obviously some parents let their kids play out, that’s their choice, I’m not knocking them for it, but we won’t let them do it…because of other people around here. WB Father.Now I think you can’t let your kids out on the street, on the doorstep even. We don’t trust, you know, people. BP Mother.I do worry, but when I see other peoples’ kids playing out I think it’s alright. I keep a check on them, have family around and about. BP Father.Some of these concerns were explicitly linked to gender, by both White British and British Pakistani parents. Girls were considered more vulnerable than boys.She says “Well I’m a girl, I can do just as much” as her brother. You’re scared though aren’t you? You’re scared to let them out around here. WB Father.She does play out, but I don’t like her to wander off too far. The boy, he wanders off a few blocks. BP Father.For most families the perceived dangers of outdoor play came from the environment and strangers. However, one British Pakistani mother’s concern for girls was expressed specifically in relation to the possibility of her daughters behaving inappropriately when outdoors and away from supervision.I want them to be in an environment where there’s no temptation from boys or anything. Here they might say they are doing things like going to school or going to town and they could not be… Young girls aren’t allowed to do that stuff there [Pakistan]. BP Mother.“wherever you go”. No such concerns were expressed by any of the White British families interviewed.This position was clearly contested within the family however, as her husband interjected that temptations are found Many parents reported that their daughter walked to school, sometimes alone but more often with a friend or with parents. Specific reasons for not walking to school included the journey being difficult for younger siblings:We don’t walk, we have the car. The little one gets tired and wants carrying everywhere so it’s too much. It’s easier in the car. BP Mother.Some White British parents, but not British Pakistani parents, mentioned that their need to get to work in the mornings meant that they dropped their children off at school by car because they used their car to travel on to work. Often the need to get to both school and work in the morning meant that White British parents did not feel they had time to walk to school.I’d struggle if I didn’t have the car as I start at 8. I need to get the kids up and to breakfast cluba. It’s always a mad rush. WB Mother.Some parents of both ethnicities considered that time spent in front of television, games consoles and computers represents a threat to children’s health, and were at pains to emphasize their efforts to limit screen time, some reporting that their efforts were successful, others that they found it difficult to keep children away from screens.We don’t watch a great deal of TV. Sundays are no TV days… WB Father.She can watch TV or play on her DS for half an hour at bed-time. WB Mother.They have the attitude that they only need to come off when someone tells them to come off. I tried to control it loads of times, where you cut it off at a certain time, but they end up nagging you and that and you forget. BP Father.However, some British Pakistani (but not White British) parents expressed appreciation of the fact that during frequent visits between members of their extended family personal screens kept their children occupied, or adults and children watched television together. For example, two parents reported children watching television or taking screens or games with them during family visits:If I’m at work they probably go round to her parents’ house, spend some time there…the women just tend to sit about doing their thing and the kids, they have the games consoles to occupy them. BP Father.We don’t really make plans to go out. We have the family round and just tend to stay in and watch stuff on the telly. BP Father.We provide objective evidence that British Pakistani girls were less active than White British girls, both in terms of average activity, assessed by counts per minute, and in time spent in moderate to vigorous physical activity. In addition, British Pakistani girls spent significantly more time sedentary than White British girls. These results echo those of previous comparisons of British South Asian and White British children using accelerometry or pedometry-11, but In line with studies of adults of South Asian and White European origin in the UK-37 and dMost girls of both ethnicities reported playing outdoors (other than at school) at some point during the three days. However, British Pakistani girls were significantly less likely to report outdoor play than White British girls. The interview results suggested that some parents of both ethnicities, particularly those living in the inner-city, were concerned about their daughters playing outside because of perceived danger from traffic and other people. These kinds of concerns are often reported by parents,40,41 anBritish Pakistani girls were less likely to travel actively to school than were White British girls. Owen et al. also fouWe did not collect information on distance between home and school, which is known to be strongly correlated with mode of travel, with children living further from school more likely to travel by car. HoweverInterestingly, given that British Pakistani girls spent more time sedentary than White British girls, fewer British Pakistani girls than White British girls reported daily screen-time of over two hours. However, there was no statistically significant difference between the groups in adjusted models. Others have also reported no difference in self-reported screen-time in British South Asian and White children,20. It aizzat’ (family status and honour) played a key role in the willingness of adolescent girls/young women to participate in physical activities outside the home. As Kay[The most prominent explanation in the literature for low physical activity levels in girls and women of South Asian origin has been the importance of South Asian/Islamic ‘culture’ and religious proscriptions in limiting physical activity outside the home. For exa. As Kay points o. As Kay,47,48. I. As Kay-51.However, such concerns were not emphasised by most British Pakistani parents in our study. There was relatively little evidence that participation in physical activity outside school in the form of mixed sex sports and exercise sessions, or in unsupervised outdoor play or walking, was actively prohibited for these young girls; one British Pakistani mother did express anxiety about ‘temptation from boys’, but this was not a widely-expressed worry among our study participants. It is certainly possible that family concerns about British Pakistani girls’ activities and social interactions may have influenced the activity levels of girls in our study (e.g. on the school playground). HoweveIt is important to acknowledge that our interview data were fairly limited. The parents of only 21 girls from among the 166 study participants volunteered to be interviewed, and just 19 interviews were carried out. Although this kind of qualitative research does not require a statistically representative sample, in that insights derived are intended to be illustrative rather than generalisable, the fact remains that this was a highly self-selected sample of parents, who may have held particular views about the value of physical activity and who were perhaps keen to share their positive experiences. In relation to the point about ‘cultural’ or ‘religious’ barriers to participation in sports and exercise, it may be that a wider range of opinions from parents might have suggested a rather different picture.Our analyses do not adjust for socioeconomic status. Owen et al. reportedWhile screen-time is likely to be the most common sedentary behaviour among young girls, future The strengths of this study lie particularly in the combination of methods, allowing not only objective characterisation of activity levels and time spent sedentary, but an exploration of the behaviours and family context underlying these observations. Thus, unlike previous studies, we have been able to provide potential explanations of our accelerometry findings.British Pakistani girls were less physically active than White British girls and engaged to a lesser extent in key activities  known to contribute to objectively measured physical activity levels in children outside school. British Pakistani girls were also more sedentary than White British girls, although there was no significant difference in the proportion of girls who watched screens for more than the widely recommended limit of two hours.Detailed insight from interviews suggests that structural constraints of family life limited the participation of girls of both ethnicities in physical activity. There was some evidence to suggest that these constraints varied between the two groups, particularly in relation to maternal employment, number of children in the family and obligations to the wider family, and attendance at mosque school. Parental concerns about safety for children were an important factor affecting participation in physical activity for both groups, while concerns about modesty, often highlighted in the literature on physical activity in girls and women of South Asian origin, had limited impact for the young girls in this sample.Interventions designed to address low levels of physical activity in young girls should recognise the importance of these structural constraints and concerns, and possible variation in constraints across ethnic groups, avoiding the temptation to focus on high profile ‘cultural differences’. For example, the targeting of opportunities for single-sex organised exercise or play to girls of South Asian origin is unlikely to be effective unless provided at a time and place that fits with the wider commitments of girls and their families.aBreakfast club is a childcare service that runs on school grounds between the hours of 7 – 9 am. This service includes breakfast and play activities for children who attend.The authors declare that they have no competing interests.All authors contributed to the design of the study. YHT collected the data and lead the analysis, assisted by TP and, for qualitative data analysis, by KH. All authors contributed to and approved the final manuscript.


While a growing body of neurocognitive research has explored the neural substrates associated with attention deficit hyperactive disorder (ADHD), an objective biomarker for diagnosis has not been established. The advent of functional near-infrared spectroscopy (fNIRS), which is a noninvasive and unrestrictive method of functional neuroimaging, raised the possibility of introducing functional neuroimaging diagnosis in young ADHD children. Previously, our fNIRS-based measurements successfully visualized the hypoactivation pattern in the right prefrontal cortex during a go/no-go task in ADHD children compared with typically developing control children at a group level. The current study aimed to explore a method of individual differentiation between ADHD and typically developing control children using multichannel fNIRS, emphasizing how spatial distribution and amplitude of hemodynamic response are associated with inhibition-related right prefrontal dysfunction. Thirty ADHD and thirty typically developing control children underwent a go/no-go task, and their cortical hemodynamics were assessed using fNIRS. We explored specific regions of interest (ROIs) and cut-off amplitudes for cortical activation to distinguish ADHD children from control children. The ROI located on the border of inferior and middle frontal gyri yielded the most accurate discrimination. Furthermore, we adapted well-formed formulae for the constituent channels of the optimized ROI, leading to improved classification accuracy with an area under the curve value of 85% and with 90% sensitivity. Thus, the right prefrontal hypoactivation assessed by fNIRS would serve as a potentially effective biomarker for classifying ADHD children at the individual level. •Objective neuro-functional biomarker to diagnose ADHD has not been established.•We measured right prefrontal fNIRS signals with a go/no-go task execution executed.•We assessed the accuracy of classification between ADHD and healthy control.•We found the way of classification with 90% sensitivity of diagnostic prediction.•Our results would provide screening tool clinically applicable for ADHD children. ADHD affects between 3 and 12% of elementary school children with behavioral symptoms being characterized by hyperactivity, impulsivity and inattention . ADHD syOne potential approach is the use of noninvasive functional neuroimaging modalities, such as functional magnetic resonance imaging (fMRI), SPECT, PET, and, most recently, functional near-infrared spectroscopy (fNIRS) to present candidate biomarkers for ADHD. Previous fMRI studies have elucidated neurological dysfunction of frontostriatal network in ADHD patients . In addiAmong modalities, fNIRS provides robust advantages such as its compactness , affordable price, tolerance to body motion and accessibility , and thuIn a series of studies making the most of fNIRS's merits, we have explored the neural substrate of inhibitory and attentional controls in school-aged ADHD children. Importantly, the cumulative post-scan exclusion rate of our previous fNIRS studies  is less than 5% of a total of 81 right-handed ADHD children and 69 control subjects including 6 years old . This isWe monitored the oxy-hemoglobin signal changes of ADHD children (6–14 years old) performing go/no-go or oddball tasks to examine inhibitory and attentional controls, respectively. We also included non-medicated, age- and gender-matched normal controls. In the control subjects, the go/no-go task recruited the right inferior and middle prefrontal gyri (IFG/MFG), but this activation was absent in pre-medicated ADHD children. The reduction of right IFG/MFG activation was acutely normalized after administration of MPH  or ATX  . RegardiFrom a genetic perspective, genetic factors also have been reported to play an important role in the development and course of ADHD disorder. In recent years considerable studies on different candidate genes for ADHD have been identified such as the catechol-O-methyltransferase (COMT) gene , the dopThus, fNIRS successfully visualized differential neural substrates for ADHD and typically developing control children in inhibitory and attentional controls in group analyses. While both go/no-go and oddball tasks recruited the right IFG/MFG, the activation was typically greater for the go/no-go task  . These fIn order to realize a highly accurate classification between ADHD and typically developing control children at an individual level, we have to deal with two issues. First, while go/no-go task-related activation in control subjects is conspicuously great at the single channel located between the right MFG and the IFG in group analyses, activation patterns vary across individuals. Such individual spatial variability necessitates that we determine which areas of the prefrontal cortices should be assessed to comprise the regions of interest (ROIs) to represent areas of inhibition-related cortical dysfunction in ADHD children. Second, although go/no-go task-related activation in the control subjects has a large effect size with group analyses, amplitude of activation varies across individuals. Thus, a cut-off amplitude for cortical activation needs to be identified for each ROI mentioned above in order to differentiate ADHD children from control children. Thus, making the best use of multichannel measurements, we adapted well-formed formulae to analyze the constituent CHs of the optimized ROIs, and assessed whether a specific logic could improve the efficacy of classification.Hence, in this study, we explore a method for individually classifying ADHD children in comparison to typically developing children by using fNIRS, emphasizing how spatial distribution and amplitude of hemodynamic response associated with go/no-go task execution can be utilized. In so doing, we explore the feasibility of providing objective diagnostic tools that are clinically applicable for school-aged ADHD children.22.1t = 3.802, p < 0.01) higher than those of pre-medicated ADHD subjects. All children and their parents gave oral consent for their participation in the study. Written consent was obtained from the guardians of all subjects according to the latest version of the Declaration of Helsinki. The study was approved by the Ethics Committees of Jichi Medical University Hospital, and the International University of Health and Welfare.Thirty clinically referred, right-handed Japanese children with a mean age of 9.1  who met the DSM-IV criteria for pre-medicated ADHD participated in the study . Some su2.2We examined inhibition-related hemodynamic cortical activation while the subjects performed a go/no-go task. We examined pre-medicated ADHD and control subjects once without medication; all pre-medicated ADHD subjects underwent a washout period of more than 2 days for MPH or ATX. The procedure consisted of 6 block sets, containing alternating go (baseline) and go/no-go (target) blocks. Each block lasted 24 s and was preceded by instructions displayed for 3 s, giving an overall block-set time of 54 s and a total session time of about 6 min. In the go block, we presented subjects with a random sequence of two pictures and asked them to press a button for both pictures. In the go/no-go block, we presented subjects with a no-go picture 50% of the time, thus requiring subjects to respond to half the trials  and inhibit their response to the other half . Specifically, the instructions read in Japanese, “You should press the button as quickly as you can. Remember you want to be quick but also accurate, so do not go too fast.” Participants responded using the forefinger of their right hand. A go/no–go ratio of 50% was selected as it has been most often used in previous neuroimaging studies . We pres2.3We used the multichannel fNIRS system ETG-4000 , utilizing two wavelengths of near-infrared light (695 and 830 nm). We analyzed the optical data based on the modified Beer–Lambert Law  as previFor statistical analyses, we focused on the oxy-Hb signal because of its higher sensitivity to changes in cerebral blood flow than that of deoxy-Hb and total-Hb signals , its higWe set the fNIRS probes so that they covered the lateral prefrontal cortices and inferior parietal lobe, referring to previous studies . Specifi2.4We preprocessed individual timeline data for the oxy-Hb signals of each channel with a first-degree polynominal fitting and high-pass filter using cut-off frequencies of 0.01 Hz to remove baseline drift, and a 0.8 Hz low-pass filter to remove heartbeat pulsations. Note that Hb signals analyzed in the current study do not directly represent cortical Hb concentration changes, but contain an unknown optical path length that cannot be measured. Direct comparison of Hb signals among different channels and regions should be avoided as optical path length is known to vary among cortical regions . From th2.5Based on the individual and channel-wise oxy-Hb signal contrast measured using fNIRS, a method for quantitative analysis was explored so as to enable a higher disease classification rate between ADHD and typically developing children. Because statistically decreased cortical activation was consistently present in 1) the right side and 2) the prefrontal area in ADHD children in our previous studies , we adopWe set the following four anatomical ROIs: Region 1 (CH 1 to 22) covered all measured channels in the right hemisphere. Region 2  included all the channels that were probabilistically located in the right prefrontal cortex either entirely or partly. Regions 3 and 4 were determined in exploratory ways; their specific constituents will be described in the Results section. Region 3 consisted of the CHs that were significantly activated during go/no-go task execution in the group analysis of normal control subjects. Specifically, we selected the experimental ROIs where significant oxy-Hb difference was observed between ADHD and control subjects in group comparison.t-tests (two-tails) on target vs. baseline contrasts. We set the statistical threshold at 0.05 with Bonferroni correction for family-wise errors. Region 4 consisted of the CHs that exhibited significantly larger go/no-go-task-associated activation in the normal control children than in ADHD children. Among the channels that constitute Region 4, we performed comparisons between control and ADHD children. We performed independent two-sample t-tests (two-tails) on these contrasts with a statistical threshold of p < 0.05. For each CH, we assessed the integral value of oxy-Hb signal. This value represents the size of the hemodynamic response during the inter-trial mean of differences between the oxy-Hb signals (4–24 s after go/no-go block onset) and baseline (14–24 s after go block onset) periods in each CH. We averaged each CH's integral value for the four ROIs presented above.Then, to screen the channels involved in go/no-go tasks in normal control and ADHD subjects, we performed paired Next, we explored setting a cut-off value to distinguish ADHD children from control children with higher accuracy. For this purpose, a conservative receiver operating characteristic (ROC) analysis was performed and used to generate simple indices of fNIRS-based oxy-Hb signal patterns for individual ADHD diagnosis.Specifically, it was determined whether 30 ADHD and 30 normal control subjects were below or above a given cut-off value for oxy-Hb signal . The subjects were further classified into the following four categories: true positive   software package.33.1Regarding all measured CHs in the right hemisphere (Region 1: CHs 1 to 22), the resulting area under the ROC curve (AUC) value was 75.1%. At the optimal cut-off value of 0.0120 mM mm, differentiation between ADHD and control subjects  was achi3.2We calculated the average of the integral value of oxy-Hb in Region 2  covering the right prefrontal cortex in 30 individual ADHD and 30 control subjects. Regarding all measured CHs in the right prefrontal cortex, the resulting AUC value was 71.0%. At the optimal cut-off value of 0.0120 mM mm, differentiation between ADHD and control subjects  was achi3.3p < 0.05, Bonferroni-corrected, Cohen's d = 0.741), R CH 6  and R CH 10  in control subjects. Conversely, ADHD conditions showed no significant oxy-Hb increase in the measured cortical areas. Thus, we adopted CHs 5, 6 and 10 as statistically specific ROIs to represent the channels activated for go/no-go task execution in typically developing control subjects. We calculated the average of the integral value of oxy-Hb signals for CHs 5, 6 and 10 in 30 individual ADHD and 30 control subjects. The resulting AUC value was 79.2%. At the optimal cut-off value of 0.0240 mM mm; differentiation between ADHD and control subjects . These channels are located in the border region between the right MFG and IFG : 59,28,19 (25), MFG 18%, IFG 52%; R CH 10, MNI coordinates x, y, z (SD): 48,37,34 (27), MFG 63%, IFG 31%) in reference to a macroanatomical brain atlas  or when his/her oxy-Hb signals in any CH in the ROI was above a given threshold (OR logic). The use of AND logic led to an improved AUC value of 0.850 and 90% sensitivity. Thus, we suggest that the use of well-formed formulae on macro-anatomically-labeled fNIRS channels is a practical option for augmenting the diagnostic predictivity for ADHD patients at an individual level.The two CHs used for the optimized prediction of ADHD patients were probabilistically located on the border of the right IFG and MFG. Evidence for abnormal function of the right IFG and MFG in ADHD patients is supported by a wealth of neuroimaging studies. fMRI studies assessing response inhibition have consistently reported right prefrontal activation . MoreoveFurthermore, several other fMRI studies have adopted ROC analysis in order to identify the biomarker an individual classification of ADHD children. Recently, an fMRI study performed assessments for the diagnosis of ADHD during a motor-related inhibition task (stop task) . Hart etIn addition, the right IFG and MFG have been reported to innervate the striatum in the dopaminergic (DA) system as well as the locus coeruleus . Therefo4.3In the present study, we selected a go/no-go task paradigm with alternating go blocks as baseline blocks and go/no-go blocks as target blocks without rest segments in between active (go and go/no-go) task blocks . Tsujii Another merit of the block-design paradigm is that the baseline blocks serve as a motor control for the target blocks. Furthermore, reminiscent of our study demonstrating the clinical utility of fNIRS-based method for individual diagnosis of ADHD children offers two significant advantages: it is simple and it is an ADHD-friendly diagnostic tool, applicable to preschool ADHD children as young as 6 years old.Several previous studies have reported on systems for individual diagnostic classification of ADHD and control subjects that apply multifactorial methods  with structural MRI data  and to fIn addition, it should be noted that fNIRS-based measurement offers a clinical advantage for the diagnosis of ADHD children: fNIRS is convenient and robust, and thus is suitable for the functional monitoring of children with ADHD, who have difficulty performing active cognitive tasks in the enclosed environments of other imaging modalities. In fact, the cumulative post-scan exclusion rate of our current study was 4.8% (three subjects among 33 ADHD and 30 control children) as in our previous studies which also presented less than 5% of a total of 81 ADHD children and 69 control subject . This isfNIRS-based clinical diagnosis of individual subjects is attracting increasing research interest with extremely promising results. A recent multi-site case-control large-scale fNIRS study involving over 600 adult patients with depressive disorder, bipolar disorder, or schizophrenia reported high classification accuracy with univariable analysis, a centroid in the hemodynamic response pattern: sensitivity of discrimination from healthy control subjects was 74.6% for major depressive disorder, 76.9% for bipolar disorder, and 90.0% for schizophrenia . In addi4.4In the current study, we present a fNIRS-based application and demonstrate its utility for individually classifying ADHD and typically developing control children. However, for appropriate interpretation of the current findings, several issues need to be addressed.First, the current study aimed at technical exploration with an emphasis on spatial analysis, and thus the sample size was relatively small. In order to validate the clinical utility of the current method, more extensive research with a larger sample size must be performed.Second, the current study does not necessarily represent an initial screening phase for ADHD diagnosis, but rather corresponds to a reconfirmation process for ADHD-confirmed patients. Therefore, for its potential use as aid for diagnosis, we need to explore disorder-specificity of fNIRS based individual classification relative to other developmental and psychiatric disorders, such as Autistic spectrum disorder, oppositional defiant disorder, conduct disorder, depression, and anxiety in the next step.Third, although all ADHD subjects had temporally stopped medication (MPH or ATX) more than 48 hours before fNIRS examination, the conditions of the ADHD subjects in this study may not exactly mimic those of naïve ADHD patients. The brain function and structure can be changed by long-term MPH and ATX administration; a recent metaanalysis on human studies using fMRI suggested that long-term MPH treatment is associated with more normal activation in the right DLPFC . Therefo5The current study intended to examine the practical utility of individual diagnostic classification between ADHD and typically developing children. To our knowledge, this is the first fNIRS-based neuro-diagnostic monitoring method enabling the distinction between ADHD and typically developing children. The fNIRS-based measurements were simple and sufficiently robust, with 90% sensitivity and 70% specificity applicable to all ADHD children (9.2 years of age on average) including those as young as 6 years. The specific prediction of ADHD children was achieved by monitoring hypoactivation of the two channels located at the right IFG and MFG during an inhibitory control task, and was reinforced by the adaptation of well-formed formulae, suggesting the utility of right IFG/MFG hypoactivation as a potential biomarker for individual diagnosis of ADHD patients. The current results clearly demonstrate the potential of fNIRS as a monitoring tool for the early diagnosis of ADHD children, although its actual use in clinical diagnosis of ADHD children should await further validation involving medication naïve ADHD patients.


To assess and compare the sensory processing abilities of children with AttentionDeficit/Hyperactivity Disorder (ADHD) and children without disabilities, and toanalyze the relationship between sensory processing difficulties and behaviouralsymptoms presented by children with ADHD. : Thirty-seven children with ADHD were compared with thirty-seven controls using atranslated and adapted version of the "Sensory Profile" answered by theparents/caregivers. For the ADHD group, Sensory Profile scores were correlated tobehavioural symptoms assessed using the Child Behaviour Check List (CBCL) and theBehavioural Teacher Rating Scale (EACI-P). The statistical analyses were conductedusing the Mann Whitney test and Pearson correlation coefficients.: Children with ADHD showed significant impairments compared to the control groupin sensory processing and modulation, as well as in behavioural and emotionalresponses as observed in 11 out of 14 sections and 6 out of 9 factors. Differencesin all Sensory Profile response patterns were also observed between the two groupsof children. Sensory Profile scores showed a moderately negative correlation withCBCL and EACI-P scores in the ADHD group. : These results indicate that children with ADHD may present sensory processingimpairments, which may contribute to the inappropriate behavioural and learningresponses displayed by children with ADHD. It also suggests the importance ofunderstanding the sensory processing difficulties and its possible contribution tothe ADHD symptomatology. Attention Deficit/Hyperactivity Disorder (ADHD) is a common developmental disorder inchildhood with an estimated prevalence of up to 6.4% in school age childrenSensory Processing (SP) is a widely used terminology in the literature to designate aneurological process, and is defined as the ability of the central nervous system toassimilate, process and organize appropriate responses to information. Sensorymodulation is the ability to regulate the degree, intensity and nature of a response toa sensory inputunder responsivity - poor reactions to relevant stimuli inthe environment in the form of passivity, apathy, or lethargy ; sensory seeking- a constant search for intense stimuli ; andincreased or over responsivity or exaggerated, aversive or intolerantresponses to stimuli ,Individuals with sensory modulation difficulties may show behaviour patterns related todecreased or -,,These conditions may adversely affect the efficiency of the person's ability to adapt todaily situations, to interact with the environment, to participate in social skills andschool activitiesAccording to Dunn and BennettMangeot et al.From the behavioural point of view, Dunn and Bennett,,These results were also reported by Yochman et al.,not paying much attention to the role of the sensorimotor dimension. Although fewstudies in the literature have indicated the presence of SP difficulties in ADHDchildren, most researchers have worked with a general profile, and few have exploredfurther characterizations of all components of Sensory Processing. More research isneeded to explore and characterize SP impairment patterns in ADHD children, and toverify the impact and possible relation between SP difficulties and symptoms presentedin their daily-living activities.Given the multidimensional nature of ADHD, current research has largely focused oncognitive and behavioural abilities related to attentive and executive functionsFrom a behavioural point of view, ADHD-C has been reported to compromise adaptivefunction with higher incidence of interpersonal relationship issues and externalizingbehaviour, such as aggressiveness, impulsiveness or oppositional and conduct disorders.In relation to internalizing behaviour, such as anxiety, somatic and other problems, thedifferences between subtypes tend to decreaseChu and ReynoldsThe sample consisted of 74 children, aged 6-11  years, whose parentswere the informants. Thirty-seven children with ADHD  were recruited from an outpatientclinic, associated with the Universidade Federal de São Paulo (UNIFESP), São Paulo,SP, Brazil, that specialized in the diagnostic of children and adolescents. Thechildren were referred to a multidisciplinary clinical assessment schedule thatconsisted of psychiatric, neurological and neuropsychological evaluation.The neuropsychological assessment included the following: the children's intellectuallevel was tested using the abbreviated (estimated IQ) Wechsler Intelligence Scale forChildren (WISC-III)The sample was recruited immediately after the diagnosic assessment, prior to thebeginning of the medications. Children with pervasive developmental disorders,psychiatric disorders , neurologicaldisorders ,intellectual disability (IQ<70) and those who were prescribed drugs for ADHD, wereexcluded.A DSM-IV-TR-based questionnaire answered by the parents/caregivers found that 21.6%(n=8) of the sample met the criteria for the inattentive subtype (ADHD-I); 19.9%(n=7) for the hyperactive/impulsive subtype (ADHD-H/I); and 59.5% (n=22) for thecombined subtype (ADHD-C). The results of the CBCL showed that 13.5% (n=5) presentedno comorbidity indicators and 86.4% (n=32) had one or more ADHD-associatedcomorbidity indicators. Of the 32 children, 40.6% (n=13) met criteria for AffectiveDisorder indicators; 40.6% (n=13) for Anxiety Disorder indicators; 15.6% (n=5) forSomatic Disorder indicators; 65.6% (n=21) for Opposition Defiant Disorder indicators,and 68.7% (n=22) for Conduct Disorder indicators.The control group consisted of 37 children paired with the ADHD group by age, genderand type of school .The control group was a convenient sample recruited by the parents/caregivers of theADHD group by asking classmates and neighbours to participate. We excluded childrenwith hyperactivity and/or inattention indicators, based on the abbreviated ConnersRating Scale (CATRS-10)Sensory processing abilities were assessed using a version of the SensoryProfileThe questionnaire contained 125 items divided into 14 sections, 9 factors and 4response patterns. The 14 sections were divided into three categories: 1) SensoryProcessing, 2) Modulation and 3) Behavioural and Emotional Responses. The 9 factors -Sensory Seeking, Emotionally Reactive, Low Endurance, Oral Sensory Sensitivity,Inattention/Distractibility, Poor Registration, Sensory Sensitivity, Sedentary, FineMotor/Perceptual - were based on combined scores from specific items from differentsections. The 4 response patterns - Low Registration, Sensation Seeking, SensorySensitivity, Sensation Avoiding - were combined scores from specific factors andsections.The questionnaire used a 5-point Likert scale, corresponding to the frequency of eachbehaviour (1=Always to 5=Never), where a lower score indicated a higher frequency ofundesirable behavioural responses to the sensory events.Behavioural symptoms of the ADHD children were examined using the Child BehaviourChecklist (CBCL)All procedures in this study were approved by the ethics committee of UNIFESP (CEP1555/09). Informed consent forms were obtained from the children and theirparents/caregivers.The Sensory Profile questionnaire was administered to both groups in a singleinterview after receiving the written consent of the parents or caregivers. Data forthe ADHD group of children were collected at an outpatient unit associated with theUNIFESP, while the control group data were obtained at their homes or schools.Cohen d) was also calculated todetermine the strength of the observed differences between variables.Since normal distribution was not confirmed for most variables, the non-parametricMann Whitney test was used to compare the ADHD and control groups' scores, and theKruskall-Wallis test was used to compare the ADHD-I, ADHD-HI and ADHD-C subtypescores. The magnitude effect , were found between the ADHD and control groups on 11 of the 14 Sensory Profilesections .Significant differences, with moderate to large magnitude effect , were also observed on 7 of 9 the factors . The anaNo significant differences were found between ADHD subtypes on the Sensory Profilesections, factors or response patterns, except for the multisensory section , in which ADHD-C  scored lower than ADHD-I and ADHD- HI .Pearson's correlation analysis detected a moderately negative correlation  between the ADHD group's CBCL and Sensory Profile scores. Forinstance, higher indicators of comorbidity disorders were associated with poorerresponses on some sensory processing aspects . This coA moderate significant negative correlation  was also foundbetween the EACI-P and Sensory Profile scores, suggesting that increased signs ofbehavioural impairment at school were associated with worse responses on some aspects ofthe SP . This coSensory Profile abilities of ADHD children were assessed according to their response todaily sensory events. In addition, the possible relationship between sensory processingimpairments and behavioural symptoms presented by ADHD children was also analyzed.Our results indicated significant differences on 11 of the Sensory Profile's 14sections, on which ADHD children scored lower. These results are consistent with thosereported by authors who have used the same instrument. Dunn and Bennett,,Others authors have also reported similar findings to our own, such as significantdifferences between ADHD and control groups for auditory, visual, touch and oralprocessing, indicating that ADHD children may have sensory processing difficultiesrelated to these systemsIn regard to Sensory Profile factors, we found significant differences between ADHD andthe control group scores in 7 out of 9 factors, the exceptions being oral sensitivityand sensory sensitivity. Yochman et al.Our study also found that ADHD children experienced major difficulties showingsignificance in all four Sensory Profile response patterns: Sensation Avoiding, SensorySeeking, Sensory Sensitivity and Poor Registration. This dimension was previouslyanalyzed only by Dove and DunnCheung and SiuThis study found that ADHD children had significant Sensory Processing impairments ondimensions such as emotional/social responses (section L) or Emotional Reactivity(factor 2), containing items related to self-esteem, frustration tolerance,irritability, anxiety and other emotional aspects. Some authors suggest that thesebehaviours may be associated with ADHD children's executive function deficits, impedingadequate performance of daily tasks and social skillsSensory processing impairments were also observed on dimensions such as vestibularprocessing (section D), modulation of body position and movement (section G) and SensorySeeking (factor 1), particularly for items concerning under-responsivity to vestibularand proprioceptive systems, showing excessive body movement and continuous stimulusseeking. These results pose the question of whether ADHD symptoms, such as constantlyseeking body movement and stimuli, as described by the DSM-IVOur results also showed impairment on auditory-processing items (section A), whichreflect overly responsive behaviours but also under-responsivity. It is important toconsider that some of the issues regarding Sensory Profile auditory processing arealready described in DSM-IV . However, assessment of auditory processing can help inunderstanding the basis of the behaviour of distractibility. Furthermore, our resultssuggest that a low threshold for sensory stimuli could contribute to distractibility inrelation to an auditory stimulus in some ADHD children, whereas a high threshold couldcontribute to inattentive behaviour in others.Therefore, from the Sensory modulation perspective, inattention could be present inindividuals with under-responsivity (i.e. high threshold) who require more intensestimuli. Distractibility could be present both in under-responsive individuals who tendto seek stimuli in order to be organized, and in over-responsive (i.e. low threshold)individuals, who respond to all stimuli, with both types presenting higher activitylevels.Significant impairments were also observed in all four-response patterns. According toDove and DunnCBCL and EACI-P scores showed moderate negative correlation with Sensory Profile scores,suggesting that the increased presence of behavioural-symptom indicators were associatedwith worse responses for some aspects of the Sensory Processing. This correlation wasfound between: Auditory processing and Affective Disorder, Conduct Disorder (CBCL), andinattention (EACI-P); Vestibular processing and Attention Disorder (CBCL); Multisensoryprocessing and Conduct Disorder (CBCL); Fine Motor/Perceptual and independentfunctioning and inattention (EACI-P); touch processing and Sensory Sensitivity, andAnxiety Disorder (CBCL) and hyperactivity/conduct problems (EACI-P); Sensation Avoidingand hyperactivity/conduct problems, inattention, socialization problems (EACI-P);Thresholds for Response and neuroticism/anxiety and socialization problems (EACI-P);and, Modulation of movement affecting activity level and socialization problems(EACI-P).Mangeot et al.According to Roberts et al.,,From the Sensory Modulation perspective, there is an interaction between the externaldimension corresponding to culture, environment, relationships and tasks, and theinternal dimension, which includes sensation, emotion and attentionOur findings did not indicate significant differences between ADHD subtypes on SensoryProfile scores, except for multisensory processing (section E). Engel-Yeger andZiv-On,,,,Previous studiesFurthermore, the present study's findings suggest that ADHD children may have sensorymodulation impairments which may contribute to behaviour and learning inappropriateresponses displayed by children with ADHD, suggesting the importance of considering andstudying SP difficulties and the possible contribution to the symptomatology of ADHD. Inclinical practice, this discussion is relevant because it suggests the possibility ofconsidering and including sensory strategies and resources when treating the symptoms ofchildren with ADHD.Our results should be interpreted in light of certain limitations, since the smallnumber of ADHD subtype subjects prevented effective comparison of theirsensory-processing abilities. Future research requires a larger sample to investigatesensory modulation differences between ADHD subtypes. Another limitation was the extentof comorbidities in ADHD children hindering more specific SP analysis. It also might beof interest to analyze the degree to which the sensory processing symptoms improve whenaffected by medication.


Transactions of the Royal Society of Tropical Medicine & Hygiene. This special issue accompanies the now biennial, 3-day meeting of the Royal Society of Tropical Medicine and Hygiene (RSTMH), which will take place at Oxford Town Hall  from 25–27 September 2014.It is a pleasure to write the editorial for this issue of 1 is to set the theme for the biennial conference with the Chief Executive and to seek approval of the Trustees. We quickly chose the theme ‘Measuring Progress’ for the 2014 meeting and will elaborate here the reasons for that choice.2 Given the lead time in preparing for the conference, our deliberations were being held at the time of the publication of the 2013 annual letter of the Bill & Melinda Gates Foundation titled, ‘Why Measurement Matters.’3 The ideas in the annual letter were, in part, catalysed by William Rosen's thesis,4 that it was improvement in and use of precise measurement tools, which allowed the innovations necessary to efficiently harness steam power and thus spur the industrial revolution. This idea, along with insights gained leading various activities of the Bill & Melinda Gates Foundation, were combined to provide a compelling argument for setting clear goals in global public health and determining the mechanisms to measure progress towards them. The idea resonated with the current President, an epidemiologist long-interested in measuring progress in reducing the burden of a range of infectious diseases, mostly by cartographic methods.8 The ideas also resonated with the RSTMH leadership. It was clear that measuring progress was a theme that could embrace the full diversity of our fellowships' interests  and thus be of wide appeal. The idea was duly adopted and is duly attributed here.On assuming office, one of the first presidential dutiesOver our 3 days in Oxford, therefore, the focus will be on how to measure progress in our diverse global health activities to ensure we are having maximum impact. To help the RSTMH in this objective we have been fortunate to attract a distinguished array of keynote speakers and session chairs (more details below), who have helped shape the scientific content of the sessions around the theme of measuring progress. The full details of the programme are available on the conference website (https://rstmhmeasuringprogress.org/scientific-programme) and include representatives of the development aid community, the charitable sector, international funding agencies, as well as the more usual suspects from academia.Editor of Governance, and; 8. Animal health and zoonoses: measuring progress in One Health, chaired by Professor James Wood, University of Cambridge.All conference participants have been encouraged to translate the measuring progress theme into their own working areas. The final selection of these working areas are 1. The global burden of disease: measuring morbidity and mortality at finer temporal and spatial scales, chaired by Professor Chris Murray, Institute for Health Metrics and Evaluation; 2. Resistance: measuring resistance to insecticides and drugs, and its threat to progress, chaired by Professor Janet Hemingway, Liverpool School of Tropical Medicine; 3. Sexually transmitted diseases: measuring progress in control, chaired by Professor Peter Piot, London School of Hygiene and Tropical Medicine; 4. Development aid: measuring progress achieved with international financing, chaired by Professor Chris Whitty, Department for International Development; 5. Neglected tropical diseases: measuring progress towards 2020 control and elimination goals, chaired by Professor Don Bundy, The World Bank, and Julie Jacobson, Bill & Melinda Gates Foundation; 6. Trials: methods for measuring progress and impact, chaired by Professor Helen McShane, University of Oxford; 7. Charitable sector: strategies for measuring progress, chaired by Dorothy Dalton, 9We are delighted to announce that Her Royal Highness, The Princess Royal, Honorary Fellow of the RSTMH, will be supporting the themes of the conference by attending sessions on the morning of Friday 26 September. We remain extremely grateful for The Princess Royal's long-standing support of the Society's work and very much look forward to welcoming Her Royal Highness to Measuring Progress. We also extend our warm welcome and thanks to Alan Magill, in his capacity as President of the American Society of Tropical Medicine and Hygiene, and Jeremy Farrar, as Director of the Wellcome Trust, as our distinguished keynote speakers. Chris Murray will also be giving the RSTMH Chadwick Memorial Prize Lecture, ‘Measuring progress in malaria: challenges and options for the future,’ in the Sheldonian Theatre on the evening of Wednesday 24 September. This re-established prize lecture will now be given at each biennial in memory of Sir Edwin Chadwick KCB (1800–1890), an English social reformer who did a huge amount to improve sanitary conditions and public health in Britain.The RSTMH strives to continually develop communications with our fellowship so we will be streaming live as much of the conference content as possible so that those unable to travel to Oxford can participate. In addition, we are extremely grateful for the generous support from the Bill & Melinda Gates Foundation and the Wellcome Trust that has enabled us to offer nearly 30 travel scholarships to early-career investigators from across the developing world to attend and present at Measuring Progress.This editorial similarly serves as a valuable communication tool with the fellowship. We use it to highlight a collection of three commissioned commentaries and a further three reviews that reflect many of the themes of the Measuring Progress conference.10 Dalton then comments on the current crisis in human resources for health in Africa.11 The focus is on well-documented success stories on how to build a sustainable workforce and thereby provide insights on how progress can be maximised by learning from these examples. Deribe and Davey conclude the commentaries by looking back,12 in this case to an article published in Transactions of the Royal Society of Tropical Medicine & Hygiene by Price in 1976: ‘The association of endemic elephantiasis of the lower legs in East Africa with soil derived from volcanic rocks.’13 They show how this article has defined progress in podoconiosis research and, moreover, 30 years after publication, progress can be measured by the use of historical data to understand trends in disease distribution. This commentary reflects an area in which we are very keen to commission contributions; tracing the impact of influential articles in the Transactions of the Royal Society of Tropical Medicine & Hygiene archives. Articles have been published continually since 1907 and are freely, available electronically to the fellowship.Hill and Whalen start the commentaries by reflecting on the importance of socio-economic factors associated with TB incidence changes and how these complicate measuring progress of DOTS .14 Babu and Babu continue the measuring progress theme by systematically reviewing coverage and compliance with mass drug administration under the programme to eliminate lymphatic filariasis in India.15 Gaps between coverage and compliance are documented, as well as correctable causes, which need to be addressed if the Indian programme is to measure progress towards elimination of lymphatic filariasis. Mnzava et al. pick up many of the themes of the conference by arguing that global reductions in malaria burden could be undermined by a lack of field-orientated vector biologists with skills to manage insecticide resistance, monitor intervention coverage and identify areas of residual transmission and that measures should be adopted to address this shortage.16Gutiérrez starts the review section by looking at factors that have contributed to reducing the impact of snakebite envenoming in Latin America and the Caribbean in the last decade and challenges for measuring progress in the next decade.We hope that the conference (in person or online) and this editorial will go some way to promoting the measuring progress theme among our fellowship. We further hope to document the enduring impact of this measuring progress theme on your work in future contributions to our flagship journals.International Health, will be available in a free online Measuring Progress collection until the end of October 2014 at http://trstmh.oxfordjournals.org/.The Measuring Progress papers in this issue, along with papers from our sister journal,


The aim of the present literature review is threefold. (1) We will review theories, models, and studies on symptomatic hyperactivity and motoric activity in attention-deficit/hyperactivity disorder (ADHD). (2) Another focus will be on assessment methods that have been proven to be effective in the detection of hyperactivity and motoric activity in children, adolescents, and adults with and without ADHD and emerging areas of research in the field of ADHD. We will compare subjective methods  and objective methods . (3) Finally, physical activity intervention studies aiming at a modification of activity and overactive behavior will be summarized that seem to be promising candidates for alleviating hyperactivity symptoms in children, adolescents, and adults with ADHD. Hyperactivity and a general increase in motoric activity with respect to amount/frequency and variability of activity/movements are main symptoms of both the combined and the hyperactive–impulsive type of attention-deficit/hyperactivity disorder (ADHD). Children with ADHD fidget with hands and feet, have difficulties remaining seated, run about, or climb excessively, and have difficulties to engage in activities quietly . ChildreCounterintuitively though, children and adolescents with ADHD appear to be less likely to engage in regular vigorous PA and organized sports . ResearcWhile inattentiveness as a core symptom has been investigated in numerous studies, no clear characterization of symptomatic hyperactivity and motoric activity in children, adolescents, and adults with ADHD exists. In addition, there are no guidelines for the assessment and intervention of motoric activity in ADHD .Therefore, aims of the present literature review are (1) to review studies on symptomatic hyperactivity and motoric activity in ADHD on different developmental stages. This is important because hyperactivity as a symptom and its possible differentiation from other symptoms as for instance inattentiveness might alter over the lifespan. (2) Another focus will be on subjective and objective assessment methods that have been proven to be effective in the detection of hyperactivity and motoric activity in ADHD. This is important because there is a need for adequate and efficient detection methods of hyperactivity in clinical settings. As we will explain in more detail, clinical diagnoses often rely on retrospective parental and teacher reports on hyperactive behavior shown by children and adolescents. (3) Finally, PA intervention studies aiming at a change or modification of activity and overactive behavior that seem to be promising candidates for alleviating hyperactivity symptoms in ADHD will be summarized.Hyperactivity constitutes a core symptom of ADHD that represents one of the most common disorders in childhood and adolescence, with approximately 5.3% of school-aged individuals being affected . The DiaSeveral psychological theories exist that address the question of what gives rise to symptomatic hyperactivity and motoric activity in ADHD. In our review we outline three current theoretical conceptions: (1) the State Regulation Model, (2) Multiple Pathway Theories, and (3) the Dynamic Developmental Theory of ADHD.The State Regulation Model suggests that clinical levels of ADHD symptomatology can be traced back to a deficit in keeping optimal activation states , 11. Buitiming, inhibition, and delay deficits to give rise to highly heterogeneous expressions of ADHD symptoms. With regard to timing, the model suggests that individuals with ADHD display deficits in, for example, the discrimination of durations and the adequate anticipation of time intervals. Inhibition deficits refer to impaired abilities of individuals with ADHD to inhibit responses in inappropriate situations  is hyperactivity essentially just an expression of one underlying ADHD condition? (b) Is hyperactivity distinguishable from, yet related to impulsivity and/or inattention? (c) Or is a combination of those two perspectives possible? These questions concern our understanding of hyperactivity, how it relates to functional impairment and, by association, what kind of treatment may work and for whom. Important statistical methods to address the ongoing debate are factor analyses, which directly concern the measurement structure underlying ADHD symptoms and thus the question of coherence and distinctness between hyperactivity, impulsivity, and inattention. Factor models that have been discussed are (a) the one-factor model, which assumes one underlying unitary symptom domain, (b) the correlated factor models, which assume distinct symptom domains that are correlated, and (c) bifactor models, which incorporate both an underlying unitary symptom domain and additional specific independent symptom domains.A one-factor model assumes that there is a single dimension underlying hyperactivity as well as impulsivity and inattention. No structural distinction is made in this model between hyperactivity and other symptom domains. However, there has been abundance of empirical evidence from factor-analytic investigations convincingly showing that this factor model does not represent an adequate conceptualization .Correlated factor models emphasize the separability of hyperactivity from inattention and/or impulsivity . They coFactor-analytic studies until the beginning of the 21st century found strong support for correlated factor models being better statistical representations of the symptom structure than the one-factor model . DirMore recently than the one-factor model and correlated factor models, bifactor models have been proposed. They simultaneously account for both the common variance  between hyperactivity and the other core symptoms of ADHD with a latent general (g) ADHD factor, and the unique separable variance of hyperactivity, impulsivity, and inattention with specific domain factors (s) that are independent  from the general ADHD factor. Thus, to a greater extent than correlated factor models, bifactor models promote the common variance between symptom domains suggesting a unitary core construct underlying all ADHD symptoms, while endorsing additional covariation that manifests in orthogonal, specific symptom factors. During the last years, a number of studies have compared the more traditional correlated factor models with bifactor models and generally supported the superior model fit of the latter across a wide range of age groups , informants , methods of measurement , and target populations .In sum, and although further studies and possibly the development of further items to assess impulsivity are needed to shed light on the question of separability between hyperactivity and impulsivity, a bifactor model framework seems to be a valid account of hyperactivity in the context of ADHD and its phenotypic representation for the following reasons: First, studies addressing the question of the development of ADHD across the lifespan reveal that it shows a substantial degree of stability and in many cases persists into adulthood , even thExcessive motor activities are a first precursor of ADHD and often observed by parents during toddlerhood, even though they are hardly distinguishable from highly variable normative behaviors during this developmental period. Most frequently, impairing levels of hyperactivity are identified during the primary school years with a majority of children (approximately 60–85%) continuing to meet diagnostic criteria of ADHD throughout childhood and into adolescence and adulthood . NotablHowever, until to date, knowledge about the development of hyperactivity in the context of ADHD is limited and mainly based on retrospective self-reports of symptoms  which mPhysical activity can be assessed with a variety of measures, including subjective self-reports via survey questionnaires or more frequent daily or hourly recalls, and more objective measures such as wearable sensors , doubly labeled water, and direct observation.So far, there is no single gold standard of measuring activity across populations  and across different assessment purposes . With wSelf-report questionnaires are still the most commonly used method for measuring activity due to their cost effective application and low participant burden. Several reviews give an overview of available measures in children and adolescents –65 and aSensor-based activity assessment with pedometers and accelerometers has gained popularity in research –76 and iSmart phones have built-in accelerometers and can be used to measure activity. However, their assessment is less precise because the phone is not consistently worn in the same position . In the same vein, GPS assessments can be used but again are less precise about micro-movements. Heart rate sensors provide a comprehensive method of measuring physical exertion that captures many activities, not only vertical movement. Other methods for determining activity, such as doubly labeled water, multichannel devices combining accelerometers with respiration rate, electrocardiography, or electromyography and direct observation are expensive and can be burdensome for participants and are therefore more frequently used in smaller studies within the lab.So far, there are few studies that have used sensor-based activity assessments in children and adolescents – and even fewer in those with ADHD. Thus, particularly in individuals with ADHD the cut-off points and algorithms for counting a movement as motion and classifying activity are still being developed. However, children diagnosed with ADHD differ from those without ADHD in the amount and intensity of their movements, as has been shown with different techniques of measuring movements, such as motion tracking systems using infrared motion analysis . InThese considerations indicate that fine-grained data from modern accelerometers, analyzed with SVMs, could be beneficial in two respects. First, they could be used to accurately identify children with ADHD with relatively little effort, in terms of both time and money. Second, those characteristics of accelerometer data that prove to be useful for distinguishing participants with and without ADHD could be further analyzed. By doing so, new insights about the nature of ADHD and hyperactivity could be obtained. These insights would go beyond the concept that was used in previous accelerometer studies on ADHD, namely that children with ADHD merely show higher amounts and intensities of activity. Hence, they could help to refine the concepts related to ADHD and hyperactivity , making them more objectively accessible, and less susceptible to subjective ratings.Vigorous PA interventions in general address several areas that are problematic for children, adolescents, and adults with ADHD. For instance, short- and long-term interventions for increasing vigorous PA have led to improved mood and improved executive functioning , especially to improved inhibition performance in children, adolescents, and adults –94. HencHowever, only few observational and single-case studies have reported improved attention and reduced hyperactivity  in children with ADHD following regular PA sessions . Only reRegarding long-term PA, Gapin and Etnier  found thThe aforementioned studies demonstrate potential positive effects of PA interventions in children with ADHD. However, all studies are clearly underpowered and replication studies are warranted. Moreover, this claim for replication studies is underscored by a recent meta-analysis investigating the effects of acute bouts of PA in children with ADHD in laboratory and field studies, which revealed inconclusive results . Some laThe meta-analysis also revealed that with respect to school settings, no effects of PA  on dependent variables such as measures of attention could be found. However, subgroups as elementary school children  seem to per se.Thus, direct positive effects of acute and chronic PA interventions on ADHD symptomatic behavior including hyperactivity seem to be possible. Still, there is scarce research revealing heterogeneous results. An important research question that is still unanswered is whether there is a direct route of improving ADHD symptoms  via PA or an indirect route improving for example executive function deficits leading to a subsequent improvement of ADHD symptoms. In the same vein it might also be the case that comorbid emotional deficits as for instance affective lability shown by children and adolescents with ADHD is altered via PA interventions leading to subsequent improvement of core ADHD symptoms . More specifically, ADHD and affective problems are common co-morbidities in youths  and it mThe association between vigorous PA and improved affect is well established and PA interventions have been shown to have positive effects on affect in healthy adults . While tConcretely, three studies have addressed the effects of PA on affective symptoms shown by children with ADHD so far. Jensen and Kenny  randomlyA bifactor model that represents a general ADHD symptom factor as well as independent specific symptom factors seems to be the best model to characterize the disorder. With regard to measuring hyperactivity as one of the ADHD symptoms only few studies have used sensor-based activity assessments in children and adolescents with ADHD. However, fine-grained accelerometer data analyzed with SVMs could potentially be useful to distinguish children with hyperactivity symptoms from those showing no hyperactivity symptoms. Moreover, further studies might also want to investigate the influence of medication (with MPH and Atomoxetine) and cognitive behavioral therapy on accelerometer data analyzed with SVMs. This is important because results could potentially inform about optimal treatments for individual children . In order to gain further insight into the usefulness of PA interventions for children with motoric activity and hyperactivity as in children with diagnosed ADHD, it is important to investigate effects of PA regarding several aspects. First, the effects of everyday PA  and organized, structured sports need to be disentangled. Second, the dose–response relationship is in the need of being investigated: How often and for how long should children with ADHD take part in physical activities to receive an optimal level of their symptomatic behavior . Third, interactions of medication with MPH or Atomoxetine and PA are understudied as well.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.


The resulting cross metathesis products were hydrogenated in a tandem fashion employing the residual ruthenium from the metathesis step as the hydrogenation catalyst. Interestingly, the epoxide ring remained unreactive toward this hydrogenation method. The saturated compound resulting from the cross metathesis of 1 with methyl acrylate was transformed by means of nucleophilic ring-opening of the epoxide to furnish a diol, an alkoxy alcohol and an amino alcohol in high yields.The cross metathesis of 1,2-epoxy-5-hexene ( Z-olefins . As. As1 wit2 and 3. Typically, there are several ways to perform the hydrogenation of a carbon–carbon double bond resulting from a cross metathesis reaction. A possibility consists in the Pd/C catalyzed hydrogenation of the isolated product. This method presents the advantage of being effective at room temperature under a low hydrogen pressure [242 was prepared as described here above  was necessary to reduce the carbon–carbon double bond. Nevertheless, under these conditions, the epoxide-containing product 5 was isolated in a satisfactory 46% yield for two steps without any traces of alcohol detected in the crude 1H NMR of the reaction.With these two compounds in hands, we turned our attention to their post-metathesis transformations. First, we looked at the hydrogenation of the carbon–carbon double bond in compounds pressure –38. Howepressure . A seconpressure ,22–2440.essure 24. To the re above  and the wo steps . The tan4 by ring-opening of the epoxide moiety. The diol 6, methoxy alcohol 7 and amino alcohol 8 were thus prepared by reacting 4 with water, sodium methoxide and aniline, respectively (6 proceeded cleanly and did not require any purification procedure (see 7 proceeded cleanly and delivered a single regioisomer 7 in quantitative yield. Finally, the amino alcohol 8 was also obtained as a single regioisomer in 61% yield (With this protocol secured, we turned our attention to the synthesis of useful polyfunctional building blocks. Thus far, the post-transformation of the electron- deficient olefin cross metathesis partner has received attention for the synthesis of polymer precursors. For instance, we have reported the reduction of the nitrile functional group into primary amine  and the ectively . The syndure see . Similar1% yield .trans-esterification from 6, 7 and 8, as well as cyclic amines by intramolecular cyclization involving primary amine resulting from hydrogenation of the nitrile functionality in 5. All these aspects will be further developed in our group.We have shown through selected examples that cross metathesis of an epoxide containing olefin with electron-deficient olefins constitutes a versatile entry towards trifunctional building blocks by ring-opening of the epoxide. We have shown that the tandem cross metathesis/C=C hydrogenation yielded the hydrogenated compound without altering the epoxide moiety that was further efficiently transformed into a 1,2-diol, a 1,2-alkoxy alcohol and a 1,2-amino alcohol. This strategy opens the way for numerous potential transformations involving the epoxide but also the functional group of the electron-deficient olefin. In particular, lactones should be accessible by intramolecular File 1Full experimental details and characterizations.