Children and adults with Attention-Deficit Hyperactivity Disorder (ADHD) fail in simple tasks like telling whether two sounds have different durations, or in reproducing single durations. The deficit is linked to poor reading, attention, and language skills. Here we demonstrate that these timing distortions emerge also when tracking the beat of rhythmic sounds in perceptual and sensorimotor tasks. This contrasts with the common observation that durations are better perceived and produced when embedded in rhythmic stimuli. Children and adults with ADHD struggled when moving to the beat of rhythmic sounds, and when detecting deviations from the beat. Our findings point to failure in generating an internal beat in ADHD while listening to rhythmic sounds, a function typically associated with the basal ganglia. Rhythm-based interventions aimed at reinstating or compensating this malfunctioning circuitry may be particularly valuable in ADHD, as already shown for other neurodevelopmental disorders, such as dyslexia and Specific Language Impairment. This condition, named Attention Deficit/Hyperactivity Disorder (ADHD), is the most common neurobehavioral disorder of childhood2. Akin to other neurodevelopmental disorders such as dyslexia and Developmental Coordination Disorder1, it comes with poor school success and socioeconomic disadvantages3. While many children outgrow it, in about 50% of the cases ADHD carries over into adulthood with negative consequences at work and in everyday life4.More than 5% of children show poor concentration, impulsivity and visible signs of hyperactivity5. Distortions in duration perception and production are also reported in other neurodevelopmental disorders such as autism spectrum disorders and dyslexia7. Impaired timing is associated with poor reading, attention and language skills, and with impaired executive functions8. The neural substrates of processing event durations  include cerebellar-cortical pathways9. Structural anomalies in these brain regions  and impaired connectivity within fronto-cerebellar networks are found in ADHD10. Thus, it does not come as a surprise that the processing of event duration is impaired in ADHD.Children and adults with ADHD also struggle in perceiving and reproducing event durations. They have difficulties in telling or reproducing the duration of visual and auditory stimuli and in comparing time intervals12. The mechanism underlying beat tracking is based on relative analysis of time intervals, and referred to as beat-based timing15. Beat tracking is involved in both perceptual and motor tasks, such as detecting a subtle deviation from a beat , or moving to the beat. This ability can be selectively spared following brain damage  in the presence of impaired processing of single durations16. Beat-based timing has been linked to partly independent neuronal networks, engaging basal-ganglia-cortical circuitries17.Owing to these difficulties in encoding and producing single durations, one may conclude that children with ADHD have a poor appraisal of the timing of events altogether. This conclusion may be premature, though. Children with ADHD might still be able to treat durations when embedded in a rhythmic context, by benefitting from its predictable temporal structure . Typically, durations can be processed more easily by the healthy brain when they form a rhythmic structure18; children with ADHD revealed poorer performance than controls in detecting a change in duration in a rhythmic sequence. However, it is still unknown whether children with ADHD are generally impaired in beat perception in a wider array of tasks , and whether they can take advantage of a rich rhythmic stimulus  as compared to a simpler one . This possibility is particularly appealing, as rhythmic auditory stimulation may serve as a remediation strategy in ADHD, as done for other timing disorders, such as Parkinson’s disease19, which displays poor beat tracking skills15. Interestingly, ADHD has been consistently associated to structural anomalies of the basal ganglia and malfunctioning basal-ganglia-cortical connectivity both in children20 and in adults21. For example, structural asymmetries in the caudate22, and lower activity in the putamen23 are reported in ADHD. The caudate is known to be preferentially linked to cognitive functioning, while the putamen is more closely related to sensorimotor functions24.To our knowledge, the possibility that children with ADHD may have difficulties in rhythm perception has been suggested by one previous study25. Children were asked to detect subtle deviations from the beat of music and metronome sequences. Moreover, they tapped with their finger to the beat of the same stimuli. For comparison, their ability to perceive single durations (duration-based timing) was tested by asking children to compare the duration of pairs of tones.Due to the aforementioned malfunctioning of basal ganglia-cortical circuitries in ADHD, it is expected that children with this condition may have difficulties to track the beat, on top of their poor appraisal of single durations. To test this hypothesis, children with ADHD were asked to track the beat of rhythmic sound sequences. Metronomes and music were used to test the ability to both track a periodic sound and to internally generate a beat (based on music), a function typically associated with the activity of the basal ganglia1 on top of ADHD. DCD is characterized by impairments in activities that require motor coordination, and co-occurs with ADHD in about 50% of the cases30. There is evidence that children with DCD, like children with ADHD, are more variable in rhythmic motor tasks such as tapping than healthy controls31. Increased abnormalities in motor network connectivity in children with both DCD and ADHD compared to ADHD alone have been recently described32. This would negatively impinge on beat tracking in motor tasks. However, DCD is not expected to affect beat tracking in purely perceptual tasks more than ADHD does. Finally, to assess whether the same pattern of timing disorders carries over into adulthood, a group of adults with ADHD was tested with a subset of the same beat tracking tasks.In addition, half of the tested children in the present study showed Developmental Coordination Disorder (DCD)Children with ADHD showed general difficulties in perceiving durations and in tracking the beat, relative to a group of matched controls . Children with ADHD performed worse than healthy controls when asked to discriminate single durations . This difference disappeared when children detected a deviant duration in the context of a periodic sequence of tones (Anisochrony detection with tones). However, poor beat tracking in children with ADHD relative to controls was apparent with music (Anisochrony detection with music) . Finally, children with ADHD had difficulties in judging whether a sound was misaligned or not with a musical beat, as compared to controls . This group difference persisted when covarying for duration discrimination performance, hence confirming a genuine impairment in beat tracking irrespective of poor coding of single durations .Summary statistics for the two groups are presented in Table t(37) = 3.50, P < 0.001, d = 1.20]. Overall, adults outperformed children on this task , and all participants, regardless of age and group, showed worst beat tracking performance when music was presented at a fast tempo than at a slow tempo . Moreover, these group differences varied with stimulus complexity . While controls tapped similarly well to both tones and music , children with ADHD showed more difficulty when tapping to the musical beat than to tones . Notably, these findings were replicated across different tempos  = 4.34, P < 0.0001, d = 1.39]. Despite the adults outperformed children on this task , ADHD participants showed greater difficulties in tracking the beat of music than of tone sequences .Adults with ADHD showed a similar pattern of results see Fig. . They syF = 11.51, P = 0.001, η2partial = 0.12]. Summary statistics for children and adults in unpaced tapping are presented in Figure F = 31.06, P < 0.00001, η2partial = 0.32]. This difference was more apparent with music than with tones  (see SI Comparison of ADHD children and adults tapping performance for ANCOVA results).As tapping to the beat involved both a perceptual and a motor component, we tested whether poor motor control alone may account for the aforementioned group differences. Indeed, children and adults with ADHD showed higher motor variability than controls when tapping regularly without sound ; hence, they are likely to pinpoint the same beat tracking skills. Notably, because performance on the BAT does not rely on fine motor control, variability in beat tracking cannot be accounted for by differences in motor control.Despite group results provided compelling evidence of poor beat tracking in ADHD and ADHD-DCD, we observed important individual differences. They can be seen when plotting individual performances in the BAT task against synchronization consistency . The BTI is a composite score based on normalized individual performances in the BAT and in paced tapping tasks of BAASTA, two tasks particularly sensitive to individual differences11. ADHD participants with a BTI score higher than −2 were classified as good beat trackers, and those with a score lower than −2, as poor beat trackers. Good beat trackers performed better than poor trackers on the Flexibility task and on the Inhibition task  = 2.15, P < 0.05, d = 0.56, and t(50.5) = 3.07, P < 0.01, d = 0.79, respectively]. In spite of the association between beat tracking and short-term memory sometimes reported36, good and poor beat trackers did not differ in that respect [t < 1]. In addition, children who were good and poor beat trackers did not differ in I.Q., and on measures of visual-selective, divided, and sustained attention, as well as on a general measure of auditory attention. Summary statistics for these measures are presented in Table Having shown variable beat tracking skills in ADHD, we tested whether this variability is associated with the severity of cognitive impairment, such as reduced flexibility, poor short-term memory, and poor inhibitory controlsee Fig.  , while taking mean and SD of their respective control groups. The BTI was calculated by averaging the z-scores obtained for the BAT and paced tapping. Participants with BTI scores lower than −2  were treated as “poor beat trackers”, while the others were considered as “good beat trackers”.Children and adults with ADHD were submitted to neuropsychological tests for assessing their cognitive functioning . Different tasks were administered to children and adults. For comparison, raw scores obtained in tests of attentional and executive functions were converted into percentiles.64. In the latter task, children repeated numbers in the same order as presented aloud by the examiner. For backward digit span, the children repeated numbers in the reverse order of that presented by the examiner. I.Q. scores were converted into composite scores . Moreover, the digit span score was converted into percentiles based on normative data.The neuropsychological tests included measures from the WISC-IV intelligence scale, from which we took into consideration the composite total I.Q. score, and the score from the digit span task, as a measure of short-term memory77. They included selective visual attention, auditory attention, flexibility, divided attention, inhibition, and sustained attention. The tests of these functions were administered in the following order.Six attentional and executive functions were assessed with the Test of Everyday Attention for ChildrenSelective visual attention: in this timed task, pairs of four different types of spaceship drawings were presented. Most pairs included two different spaceship drawings. Children had to find the pairs of identical spaceship drawings (target items) as quickly as possible. Twenty target items were distributed among 108 pairs with different drawings . The performance score takes into account the number of good pairs of drawings circled and the time spent to do it.Auditory attention: children listened silently to 10 series of tones. Each series included between 5 and 16 tones. Children had to report how many sounds they heard at the end of each series. The score is the number of good responses.Flexibility: in this attention visual-switching task presented in a booklet the participant had to count the number of “creatures”  visible all along their burrow. Arrows interspersed among the creatures pointed either upwards or downwards. The children were instructed to begin counting the creatures one by one and to change the direction of their counting when the sense of the next arrow was downwards, until the last creature was presented. The final score took into account the number of good responses and the time spent to complete the task.Divided attention: children performed simultaneously two tasks, namely a visual-search task and an auditory counting task. The individual tasks are similar to the aforementioned auditory and visual selective attention tasks. The final score took into account the number of good responses both on the visual task and on the auditory task, and the total time spent to complete the test. The test took end when the children thought they found all visual stimuli they could.Inhibition: in this Go/No go task, children pointed with a felt-tip pen to a series of squares drawn on a sheet as they heard short sequences of tones (from 4 to 16 tones). They were asked to stop pointing when a different timbre occurred. The rate of presentation of the tones increased progressively. The score is the number of good responses.Sustained attention: this is a test of vigilance in the auditory modality. The children monitored a stream of digits presented at a rate of one/2 sec. They were instructed to detect a particular target sequence (two consecutive “5”), and to report the digit occurring immediately before the sequence. Forty targets were presented over 16 min. The score is the number of good responses.78, for the evaluation of three attentional and executive domains: inhibition, flexibility, and short-term memory.Adults were administered the computerized Test of Attentional PerformanceThe tests were administered in the following order.Inhibition: in this Go/No go task, an up-right (“+”) cross and a diagonal (“×”) cross were presented in an alternating sequence on the screen. Participants were asked to press a button as quickly as possible only when the diagonal cross  appeared. The score corresponds to the number of wrong responses .Flexibility: in this set-shifting task, in each trial a letter and a digit were simultaneously presented on the right or left side of a computer screen. Participants were asked to press the left or right button on the keyboard as fast as possible depending on the side of appearance of the letter, then of the digit. The final score takes into account the number of good responses and the reaction time for each item.Short-term memory: this task examines the control of information flow and the updating of information in short-term memory. A sequence of digits was presented to the participants on the computer screen. Participants pressed a button when the digit visible on the screen was the same as the penultimate one. The performance score is the number of omissions made by the participant.t-tests and mixed-design ANOVAs were used to compare ADHD participants (with and without DCD) and controls on BAASTA tests. In ANOVAs, Stimulus was the within-subjects factor (tones vs. music) and Group (ADHD vs. controls) the between-subjects factor. Further ANOVAs tested the differences between children and adults, by taking Age (children vs. adults) as an additional between-subject factor. When differences between children with ADHD and ADHD-DCD did not reach significance, data were pooled. Post-hoc paired and independent samples t-tests with Bonferroni correction were performed to define observed effects. Spearman correlations instead of Pearson were performed between variables whenever the data were not normally distributed. Statistics were computed using R software79.Independent samples Supplementary information


Breads with distinct composition and texture characteristics were prepared with refined or wholegrain wheat and rye flour by using either straight dough or sourdough process. After chewing and gastric digestion in vitro, 100% wholemeal and refined rye breads prepared by sourdough method were disintegrated to a much lower extent than the wheat breads, having more bread digesta particles with size over 2 or 3 mm. Microstructure of the digesta particles of rye sourdough bread revealed more aggregated and less degraded starch granules when compared to refined wheat bread. The postprandial insulin responses, but not those of glucose, to the 100% rye breads made with sourdough method were lower than the responses to the refined wheat bread. Addition of gluten or bran in rye sourdough bread increased insulin response. PCA  analysis confirmed that the insulin response had a negative correlation with the number of larger particles after in vitro digestion as well as amount of soluble fiber and sourdough process. Since the high relative proportion of large sized particles after chewing and in vitro gastric digestion was associated with low postprandial insulin responses, the analysis of structural disintegration in vitro is proposed as a complementary tool in predicting postprandial physiology.Disintegration of rye and wheat breads during  Diets containing rapidly absorbing carbohydrates and low in dietary fiber (DF) are associated with increased risk of type 2 diabetes ,2, wherei.e., sourdough technology, have reported to show reduced GI values [Cereal products have unique solid foam structure, which is formed during processing . RegularI values . The redI values ,14,15 anI values ,11,12, bin vitro hydrolysis using alimentary amylolytic enzymes [in vitro methods took into account the disintegration and particle size of the products during gastric digestion, which is known to influence the rate of gastric emptying in vivo [et al. [in vitro digestion of starch, but the impact of end product structure was not examined. Hence, the role of food disintegration during gastric digestion in regulating postprandial glycemia is not known. In the present work the target was to show how different bread types disintegrate during oral and simulated gastric phase, and whether the particle size after gastric phase simulation can in part predict glycemic and insulinemic responses in vivo. For that, mechanical, structural and biochemical properties of various types of rye and wheat breads, their particle size after the in vitro gastric digestion, and in vivo glucose and insulin responses were analyzed. A multivariate analysis method was applied to study the relationships between bread characteristics, gastric phase particle size and in vivo responses of breads.To predict the postprandial glucose metabolism of cereal products, the rate of starch digestion has been investigated by  enzymes ,16,17,18 in vivo ,20. HernCommercial wholemeal wheat flour (ash content 1.8% d.w), wholemeal rye flour, refined wheat flour (ash content 0.6% d.w), refined rye flour, wheat and rye bran  were used for baking the breads. The commercial coarse wheat bran was further ground to obtain fine particle size .The reference bread (Bread 1) was a commercial refined wheat bread . Bread 2et al. [et al. [The amount of available starch was determined by an enzymatic kit . Sugarset al. . The ava [et al. , protein [et al. , and fat [et al. . Specifin = 17), aged 22–37 year, BMI 19.2–26.7 kg/m2, all had normal glucose tolerance [et al. [et al. [The postprandial glucose and insulin responses of the breads were tested in five different experiments each including refined wheat bread as the reference and Breads 1–3. The subjects for Breads 1 and 2 were men and women  in 6 mL of 0.05 M NaK phosphate buffer (containing 0.4 g/L NaCl) adjusted to pH 1.5 with HCl. Then the subjects rinsed their mouth with 10 mL of phosphate buffer, (pH 6.9) for 15 s and expectorated the rinsing solution into the beaker. The pH of the suspension was adjusted to 1.5 with HCl. The suspension was incubated for 30 min . Samples for the particle size analysis were removed at this stage.The digestion method for the particle-size analysis was performed according to the t et al. . An equiin vitro gastric treatment the liquid was decanted from the suspension and the residue was chemically fixed in 1% glutaraldehyde, and was embedded in agar gel and dehydrated and embedded in Historesin  as recommended by the manufacturer. Sections 2 mm thick were cut with a microtome . The sections were transferred onto glass slides and stained with 0.1% Light Green and Lugol iodine solution (0.33% I2 and 0.67% KI). In light micrographs of bread, protein appears green and starch dark. The samples were examined with a microscope . Micrographs were obtained with a SensiCam PCO CCD camera .After pepsin incubation described above the whole suspension was diluted with water to 200 mL and divided onto 20 Petri dishes. The particles were photographed by camera . The maximum diameter and the number of particles were analysed with the AnalySIS 3.0 image analysis program . To visualize the sizes of the different particles in the images, the large particles (diameter > 2 mm) are by image analysis shown green and the small ones (0.5–2.0 mm) red. Particles over 2 or 3 mm were considered as large particles . For micp values < 0.05 were considered to be statistically significant.The statistical significance of the differences in glucose and insulin responses between the breads was assessed using the nonparametric Friedman’s test followed by Wilcoxon’s test for pairwise comparisons. To control the overall level of significance, the Bonferroni adjustment was used for pairwise comparisons of the test breads to the refined wheat bread. In all analyses, A multivariate analysis method, PCA , was used to describe the variation among the data of different breads by compressing it to the most dominant factors. The score plots demonstrated the location of the samples, and the loading plot location of measured values on the usually two-dimensional map. The map is used to show the similarities, differences and groupings of the samples and their properties. The means of measured values were used in PCA. Models were produced using SIMCA-P12.0.1 software .Chemical composition, volume and acidity of the breads are presented in Breads made with sourdough method had clearly smaller specific volume and harder texture as compared to breads made with straight dough method . The volin vitro digestion of wheat straight dough bread  varied from 0.1%  to 7.9% . The relative portion of particles over 2 mm (Ø) varied from 0.2%  to 16.5% . The refined wheat and mixed wheat breads made with straight dough method  had the highest relative portion of particles between 0.2–2 mm (Ø), and also the average diameter of the large particles of the digested wheat breads was low. Commercial wholemeal rye bread  as well as experimental refined rye breads (Breads 5–7) had higher percentage of particles over 2 or 3 mm and larger average size of particles after in vitro digestion than the wheat breads. Both the proportion of large particles and average particle diameter were larger in flat bread (Bread 6) in comparison to pan bread (Bread 5), and addition of gluten to refined rye sourdough bread (Bread 7) reduced the proportion of large particles.The particle size distribution of the breads after analysis . To visuanalysis A and refanalysis B after cNo differences were found in glucose responses  of the breads, but insulin responses  of most 100% rye sourdough breads  were significantly lower than those of refined wheat bread controls . The insin vitro disintegration and in vivo response of breads. Due to the non-significant statistical differences, the AUC values for glucose were not included in the PCA test. in vitro digestion and amount of soluble fiber as well as sourdough process, indicating that samples prepared with sourdough process had a high number of larger particles and lower insulin response. Total dietary fiber content, portion size or amount of carbohydrates or proteins was not correlated with the insulin response.PCA was applied to study the relationships between chemical composition, structural features, in vitro digestibility and insulin responses. The glucose responses  did not show significant differences among the different breads, and thus multivariate analysis of glucose response and bread characteristics was not performed. Of the bread characteristics, low specific volume and high acidity, but, for example, not total DF content correlated with slower disintegration in vitro and lower insulin response in vivo. The lowering of insulin response as compared to refined wheat bread was linked to higher proportion of large particles of the bread after digestion in vitro. The result suggests that gastric particle disintegration in vitro could provide a tool for prediction of insulin response in vivo, like the glycemic index method is used to predict glycemic response. As shown by Eerdelink et al. [The results of the present study show that bread structure and acidity influence k et al. , slower in vivo. Disintegration and particle size of food products during digestion is dependent on food texture and is known to influence the rate of gastric emptying and thus the glycemic responses [in vivo chewing and subsequent in vitro pepsin incubation, as shown by the lowest number of large particles. The size of wheat bread digesta particles was typically less than 50 μm. On the other hand, the wholemeal and refined rye breads prepared by sourdough method were disintegrated in vitro to a much lower extent than the wheat breads, and the bread digestas contained several particles with size over 2 or 3 mm. Furthermore, the number of particles with the average diameter of particles over 2 or 3 mm correlated with AUC for insulin in PCA. Porosity provides one probable explanation for the observed differences between the wheat and rye breads. Standard wheat bread has a very porous structure, which enables salivary α-amylase to penetrate easily through the pores and disintegrate the bread matrix, whereas more dense rye sourdough breads were less susceptible for disintegration. Similarly in wheat bread increasing density has been linked to lowered digestibility [in vitro disintegration of bread (Breads 5 and 6). The number of particles in the in vitro digesta was higher and average diameter higher in the flat refined rye bread in comparison to corresponding pan bread, most likely due to the larger amount of crust and the denser structure of the flat bread. Addition of gluten in rye sourdough bread (Bread 7) decreased the number of large particles in vitro and increased insulin response to similar value as with refined wheat bread. As in wheat bread, gluten increases porosity and reduces firmness of the rye bread crumb structure, which most probably explains the increased disintegration and insulin response of the rye bread with added gluten. Breadmaking method, straight dough or sourdough, presumably influenced bread digestibility as well. Wholemeal mixed wheat breads  made with straight dough method had bigger volume and softer texture, showed less large digesta particles than the 100% rye sourdough breads, and comparable or higher insulin response than refined wheat bread. However, the breads made by straight dough process contained both refined and wholegrain flour, so the results are not directly comparable to 100% sourdough wholegrain rye bread.The macrostructure and texture of cereal products are known to affect their digestibility esponses . Porous esponses ,28. In ttibility ,29. FormMicrostructure of food is known to influence substrate-enzyme interactions and thus susceptibility of nutrients to gastrointestinal enzymes. Generally in wheat bread making, gluten forms the continuous phase of the bread matrix, whereas starch granules form the continuous phase of rye breads ,30. In ain vitro digestion. Interestingly, the rye sourdough bread with added bran (Bread 4) also showed increased insulin response that did not differ from refined wheat bread. The mechanism behind the result remains to be studied in more detail, but possibly the bran addition physically disrupted the dense structure of rye sourdough bread. Bran has typically high degree of insoluble components, especially insoluble dietary fiber that might have disrupted the rye crumb structure consisting of viscous network made of soluble arabinoxylan and starch and thus making bread components more susceptible for digestive enzymes. Generally, in a view of these results and hypothesis, it was rather unexpected that only the insulin responses but not those of glucose were lower to rye sourdough breads than to wheat breads. However, also previous studies have demonstrated that sourdough rye breads or other foods rich in starch products display low insulinemic response, regardless of their glucose response [In the case of wheat bread microstructure, pieces of bread after mastication were large, and gluten network was dominating and binding the dispersed individual starch granules. However, under conditions mimicking gastric digestion, the pieces of standard wheat bread were easily broken down since pepsin has access to protein phase surrounding the starch granules. This was also supported by the microscopy analysis of wholemeal rye sourdough bread were the continuous phase of swollen starch granule aggregates protected starch and protein against enzymatic hydrolysis. This can be one reason why disintegration of rye sourdough bread is reduced as compared to wheat bread. Adding gluten to rye sourdough bread (Bread 7) might also have changed the microstructure and continuous phase of rye bread with subsequent increase in disintegration by pepsin during response ,33.in vivo experiments carried over several years brings an element of variability to the results. Besides natural variation between the studies performed at different times, the subjects for testing the commercial breads (Breads 1 and 2) were young men and women and in the other experiments older women. However, all subjects were healthy and the age and gender differences in the subject groups are not expected to cause major impact on the results. There were also some weaknesses in the dataset for the comparison of the bread characteristics. For example, bread of 100% rye flour with straight dough process was not available to be compared to the corresponding sourdough rye bread. Nevertheless, the breads studied provided enough variance with respect to structural parameters to suggest that disintegration of structure during early phases of gastrointestinal digestion might be one factor contributing to postprandial control of glucose metabolism.Combining the data of several in vitro gastric disintegration and in vivo insulin, but not glucose responses. High relative proportion of large sized particles after chewing and in vitro gastric digestion was associated with low postprandial insulin responses, suggesting that analysis of structural disintegration is a complementary tool in predicting postprandial physiology.Relationships were shown between chemical and structural properties of rye and wheat breads, their


Using graph theory, we computed metrics that characterize both the global organization of anatomical networks (interconnectivity (clustering), integration (path length) and balance of global integration and localized segregation ) and their local nodal measures (participation (degree) and interaction (betweenness) within a network). Relative to Controls, ADHD participants exhibited altered global organization reflected in more clustering or network segregation. Locally, nodal degree and betweenness were increased in the subcortical amygdalae in ADHD, but reduced in cortical nodes in the anterior cingulate, posterior cingulate, mid temporal pole and rolandic operculum. In ADHD, anatomical networks were disrupted and reflected an emphasis on subcortical local connections centered around the amygdala, at the expense of cortical organization. Brains of children and adolescents with ADHD may be anatomically configured to respond impulsively to the automatic significance of stimulus input without having the neural organization to regulate and inhibit these responses. These findings provide a novel addition to our current understanding of the ADHD connectome.Although multiple studies have reported structural deficits in multiple brain regions in attention-deficit hyperactivity disorder (ADHD), we do not yet know if these deficits reflect a more systematic disruption to the anatomical organization of large-scale brain networks. Here we used a graph theoretical approach to quantify anatomical organization in children and adolescents with ADHD. We generated anatomical networks based on covariance of gray matter volumes from 92 regions across the brain in children and adolescents with ADHD ( It is characterized by developmentally inappropriate symptoms of inattention, impulsivity and hyperactivity, and is often associated with poor academic and social outcomes.6 It permits a data-driven exploration of topological organization of large-scale brain networks  in a way not previously possible, producing a number of summary metrics that describe the properties of brain networks. For instance, small-world and network efficiency measures describe the balance between information segregation and integration at the global level, while regionally specific properties such as nodal degree demonstrate the contribution of ‘hubs' that facilitate integrative processes.6 Previous studies of graph theoretical analysis of whole-brain resting-state functional magnetic resonance imaging (MRI) data have reported that children and adolescents with ADHD exhibit higher local clustering and lower global efficiency compared with Controls. This suggests that children and adolescents with ADHD may have less-optimized topological organization, reflecting a maturational delay in formation of functional networks8 . Cao et al.10 observed similar findings using white matter probabilistic tractography of diffusion imaging data in ADHD, demonstrating convergence between functional and structural organizational metrics.Graph theory analysis is a powerful method for quantifying and characterizing brain networks, and has been instrumental in a system level understanding of the brain.12 A gray matter network analysis approach using brain volume data derived from T1-weighted images measures this covariance, thus providing a developmental ‘snapshot' of the brain that is complementary to diffusion and functional MRI data.13 This is particularly relevant in ADHD, in which cortical maturational delay is one of the leading etiological theories.14 This data-driven approach may also shed light on the structural networks and the key hubs mediating specific functional impairments commonly observed in ADHD.To date, however, no study has reported on network measures in participants with ADHD using covariance between regional gray matter volumes. Structurally and functionally connected brain regions tend to show ‘in sync' fluctuations in gray matter volume over time. This phenomenon, known as structural covariance, is theorized to reflect developmental coordination or synchronized maturation between regions of the brain due to mutually trophic influences.In this study, we use T1-weighted MRI data to parcellate the brain into 92 cortical and subcortical gray matter regions in children and adolescents with and without ADHD. Volumes of these regions were used to map structural covariance networks for evaluation using graph theory. We hypothesized that compared with typically developing Controls, children and adolescents with ADHD would exhibit abnormal network topology. More specifically, we predict higher clustering and lower global integration compared with Controls. Further, we predict that network hubs will differ between these groups predominantly in regions crucial to observed clinical cognitive findings.international Study to Predict Optimized Treatment in ADHD . ADHD diagnoses were established by referring clinicians, based on Diagnostic and Statistical Manual of Mental Disorders  criteria. Research psychologists confirmed diagnosis and subtype using the parent-rated ADHD Rating Scale IV (ADHD-RS IV16), indicated by a score >1 in six or more items in the inattentive and/or hyperactive-impulsive sections of the scale. Twenty individuals with ADHD were determined to have inattentive subtype, while 19 were combined subtype. ADHD participants were aged 8–17 years, fluent in English and stimulant naive (n=14) or unmedicated for a minimum of 7 days prior to the time of testing (n=25). Referring clinicians and research psychologists used the Mini International Neuropsychiatric Interview for children and adolescents  to exclude any participants with learning disorders, current or past alcohol/drug abuse, or any comorbid axis 1 disorder. Ten participants with ADHD (29%) had comorbid oppositional defiant disorder.MRI data were drawn from 39 children and adolescents with ADHD scanned at Westmead Hospital  as part of the baseline data collection for the 18 Sample size was based on previous studies investigating whole-brain connectomics using diffusion tensor imaging and resting-state functional MRI in child and adolescent ADHD.10 No participants had a history of brain injury, any significant medical condition affecting brain function , or any contraindications for MRI. This study received institutional review board approval from the Human Research Ethics Committee, Western Sydney Local Health District and was conducted according to the principles of the Declaration of Helsinki 2008. All participants (and/or guardian when <16 years) provided written informed consent.Comparison was made against 30 age- and gender-matched typically developing Controls. Controls were screened for the absence of Axis 1 mental disorders using the MINI-KID and SPHERE-12.MRI data were acquired on a 3.0T GE Sigma HDx scanner  using an 8-channel head coil. Three-dimensional T1-weighted magnetic resonance images were acquired in the sagittal plane using a 3D SPGR sequence . A total of 180 contiguous 1 mm slices were acquired with a 256 × 256 matrix, with an in-plane resolution of 1 mm × 1 mm, resulting in isotropic voxels.http://dbm.neuro.uni-jena.de), implemented within the SPM8 package (http://www.fil.ion.ucl.ac.uk/spm). First, MRI data sets were visually inspected for artifacts and movement by two independent investigators, and all scans passed an automated quality assurance protocol within VBM8. Specifically, sample homogeneity was visually assessed to identify any outliers who were two or more s.d's of GM volume outside of the sample distribution.Pre-processing of the T1-weighted images was performed using the VBM8 toolbox  tissue probability maps were created using the template-o-matic toolbox,Warped tissue-type images were modulated to preserve the volume of a particular tissue using the Jacobian determinants derived from spatial normalization. Seven subjects  were excluded for motion artifacts that distorted the boundary between segmented gray and white tissue images, leaving a total of 34 ADHD subjects  and 28 typically developing Controls  for analysis. The mean sample homogeneity covariance for the remaining individuals in each group was 0.74, indicating high data quality  and no differences in scan quality between groups.20 We extracted data for 92 cortical and subcortical regions, defined using the WFU PickAtlas Toolbox.21 These regions include the 90 regions defined in previous graph analysis studies of structural correlation networks.23 We also included the bilateral cerebellum, as functional imaging studies in ADHD commonly report an altered dysfunction and connectivity associated with this region.24 Volumes were extracted from modulated, normalized non-linear GM images  for each region using MarsBaR.2526 to determine that results were not unduly influenced by the number of nodes or parcellation method.We referenced the nodes for analysis according to the automated anatomical labeling atlas.27  and a network density (cost) representing the fraction of present connections to all possible connections.Gray matter volumes of all 92 anatomical regions for each individual were used to construct structural covariance networks, in preparation for graph theory analysis. To compute a structural covariance network for each group, a 92 × 92 association matrix, R, was generated. To enhance clarity, regions were organized relative to intrinsic functional connectivity networks27 . These nhttp://www.brain-connectivity-toolbox.net/) and the Graph Analysis Toolbox.22 BrainNet viewer30 was used for visualization of regional analyses.Graph theoretical analyses were performed on the interregional covariance matrices using the Brain Connectivity Toolbox (Dmin: 0.02:Dmax). The minimum density was that at which the networks of both groups were not fragmented and paths exist between each node and every other node. The maximum density chosen was 0.50, as after this threshold the graphs become increasingly random.22To allow comparison of global network properties between groups and avoid biases associated with using a single threshold, the association matrices were thresholded at a range of network densities, in 0.02 steps  the characteristic path length (the mean number of connections on the shortest path between any two regions in the network and is a measure of network integration); (2) the clustering coefficient ; and (3) small-worldness .16 ADHD and Control groups were also compared on non-normalized global measures.To evaluate these topological measures, they were benchmarked against corresponding mean values of a null random graph. We generated 20 null networks from covariance matrices that were matched to the distributional properties of the observed covariance matrix using the Hirschberger–Qi–Steuer algorithm.Local nodal characteristics of individual network regions were also examined using the following measures: (1) nodal degree, which is the number of connections that a node has with the rest of the network, and (2) betweenness centrality, which is a measure of the number of shortest paths that traverse a given node and is used to detect nodes that are highly central for important anatomical or functional connections.6 A node was considered to be a hub if its degree was at least one s.d. higher than the mean network degree.23 Nodes were normalized by the mean network betweenness and degree of each group before between-group comparisons.23Hubs are nodes that are critical for efficient communication in a network, and have a key role in the regulation of information flow.P-values were calculated based on their position.31 This nonparametric permutation approach inherently accounts for multiple comparisons across densities.32 False discovery rate-corrected P-values were reported for all between-group differences to account for multiple comparisons .A nonparametric permutation test with 1000 repetitions was conducted to test the statistical significance of ADHD-associated differences in global and regional network topologies. In every permutation, each participant was randomly reassigned to the ADHD or Control group such that each group maintained their original number of subjects. We subsequently obtained an association matrix for each randomized group, thresholded at a range of network densities, which led to a binary adjacency matrix at each threshold. Network measures were calculated for all binary adjacency matrixes at each density. Differences in network measures between randomized groups were then calculated, resulting in a permutation distribution of difference under the null hypothesis. Differences in ADHD and Control network measures were placed in the corresponding permutation distribution and two-tailed Dmin:0.02:0.50), they were represented by a curve depicting change in network metric as a function of network density. Permutation tests were subsequently applied to the functional density analyses output to determine whether there were significant group differences. As comparison at every network density would result in a large number of comparisons (number of densities × number of regions of interest), the group comparison of regional measures was based on the functional density analyses result.22 False discovery rate-corrected P-values were reported for regional differences to account for multiple comparisons across the multiple nodes of interest.In addition to comparing global network measures at every density, functional density analyses were performed to make the between-group comparison less sensitive to the thresholding process. As network metrics were calculated across each of the specified densities , t(60)=−0.07, P=0.95, or gender ; Controls, 19 males (67.9%)), χ2 =0.24, P=0.78. Using the ADHD-RS, mean total symptom severity was 34.4±7.4.ADHD and Controls did not differ in age . The functional density analysis confirmed that this result was not driven by differences in correlation strengths in regional gray matter volumes that would make the analysis less sensitive to thresholding, corrected P=0.017. There were no significant differences at any network density thresholds between ADHD and Control groups for characteristic path length or small-worldness. Small-worldness was >1 across all network densities in both groups (P=0.028), and no difference in path length or small-worldness.The ADHD group exhibited an increased clustering coefficient relative to the Control group , a findih groups . CompariP=0.027; right, corrected P=0.048). The ADHD group had significantly reduced nodal degree compared with Controls in the left anterior cingulate , left mid temporal pole  and right rolandic operculum . Nodal betweenness was increased in ADHD compared with Controls in the left amygdala (corrected P=0.04) and precuneus , and right lingual gyrus , but reduced in the left posterior cingulum  and right rolandic operculum , as well as abnormalities in amygdalar dopamine receptor density.43 Increased amygdala activation and amygdala-lateral prefrontal cortex connectivity during viewing of fearful faces have been posited to underlie increased emotional sensitivity in adolescents with ADHD.44 Emotion dysregulation-related associations with resting functional connectivity between the amygdala and the rostral anterior cingulate have also been reported in children with ADHD.45 Our results provide new evidence that there may be an anatomical network disorganization surrounding the amygdala in ADHD.Children and adolescents with ADHD often exhibit symptoms of increased emotional sensitivity,46 which has been identified as part of the so-called ‘rich club'—a group of high-degree nodes that tend to be densely interconnected and have a key role in global information integration.47 This supports Ray et al.,48 who recently reported that individuals with ADHD exhibit reduced connectivity within rich-club networks. It is an important node within multiple networks, including the DMN, frontoparietal, salience and limbic networks. The frontoparietal network has an essential role in top-down regulation and enabling goal-directed processes, which is particularly relevant to the motivational and cognitive theories of ADHD.49 The cognitive division of the anterior cingulate specifically has a role in attentional processing by modulating stimulus selection and mediating response selection.50 The DMN on the other hand is thought to reflect task-independent introspection or ‘mind wandering' and has an antagonistic relationship with networks engaged during conditions demanding attention.51 As such, default mode interference may also contribute to attentional dysregulation.52In addition to emotion dysregulation, hallmark symptoms of ADHD are impaired attention and response inhibition. We found significantly reduced nodal degree in ADHD relative to Controls in the anterior cingulate cortex. A number of studies have found dysfunction of the anterior cingulate cortex in ADHD,55 In the current study, the ADHD network exhibited decreased degree and/or betweenness compared with Controls for three nodes within the DMN: the left anterior and posterior cingulate and mid temporal pole. In contrast, the ADHD group also exhibited increased betweenness for the left precuneus relative to Controls. Increased betweenness suggests that this prominent DMN node participates with disparate parts of the whole-brain network (more so than in Controls). Given the antagonistic relationship between the DMN and task-positive networks, this may support the view that default mode interference has a role in attentional impairment.A number of recent studies have found dysregulation within the DMN and its connectivity with task-positive networks in ADHD.56 and is also part of the VAN, which monitors for salient stimuli and reorients attention when appropriate.57 Reduced interactions of this region with other parts of the brain may contribute to the increased prevalence of language dysfunction in ADHD children compared with typically developing Controls.58 This region is not typically associated with ADHD pathology. In addition, we did not find the typically reported abnormalities within fronto-striatal circuits.2 This, however, may be a function of the structural covariance methodology and shines light on difference aspects of ADHD pathology.Reduced nodal degree and betweenness was observed in the right rolandic operculum for the ADHD group. This region is considered to be an important language hub46 A shift toward more subcortical but fewer cortical connections may imply that the brains of children and adolescents with ADHD are likely to be anatomically configured to respond impulsively to the automatic significance of stimulus input without having the neural organization to regulate and inhibit these responses. Although not tested directly in this study, this fits with behavioral symptoms observed in previous studies.41 Analysis of the organization and functioning of entire networks contributes to a more complete picture of neuropathology and has tremendous potential for providing new insights that would have previously been missed using a regionally focused framework. Enhanced knowledge of the mechanisms underlying ADHD through new methodologies provides greater diagnostic precision, which has potential for informing animal models of ADHD and identifying new treatment targets.Overall the findings suggest that ADHD is characterized by a disruption to the normal anatomical organization of neural circuits at the whole-brain level. This disruption involves a greater reliance on subcortical connections specifically with the amygdala, a region at the core of appraising emotional significance, at the expense of cortical hubs important to the dynamical regulation of multiple neural circuits, such as the anterior and posterior cingulate.59 which we saw in our inability to replicate the finding of increased clustering with the different parcellation method. We also found an opposing effect in our group comparison of nodal degree in the left anterior cingulate when using a different parcellation approach. This highlights the importance of parcellation approaches when interpreting data and comparing across studies. Third, our structural covariance approach is confined to a population-based definition of connectivity, which restricts direct correlation of network measures with individualized clinical and behavioral metrics. Recent findings show that rich-club connectivity and higher global network integration are associated with higher intellectual abilities in typically developing children.60 We did not have a reliable measure of IQ in this sample, therefore we cannot determine whether our results were mediated by group differences in IQ. Finally, our sample size is modest considering that ADHD is quite a heterogeneous disorder. Long-term effects of previous medication use and oppositional defiant disorder comorbidity are additional factors that may have increased biological heterogeneity within our sample and potentially impacted structural covariance.62 It will be important to replicate these findings in independent cohorts with larger sample size and more homogenous, well-characterized populations.This study has several limitations. First, our findings are based on cross-sectional observations, which limit how we can interpret maturational lag in network topology. Future longitudinal studies will be required to determine whether network topology is merely delayed or whether it represents aberrance in the neurodevelopmental trajectory. Second, we parcellated brains using the automated anatomical labeling atlas, which divides the cortex and subcortical structures into regions based on anatomical landmarks. This may be relatively arbitrary with respect to functional areas and nuclei. Nonetheless, our supplementary analyses using 347 nodes derived from functional connectivity reproduced the key regional finding of hubs distributed predominantly within ventral emotion and reward centers . AlthougDespite these limitations, our findings advance our understanding of the ADHD connectome. The findings demonstrate that ADHD is characterized by disruptions to both the global and local organization of brain morphometry. Structural covariance metrics offer promise for helping to refine the mechanistic understanding of ADHD and for potential treatment targets.


Overall mean recoveries were 86 and 95% for spiked rye and rye crispbread with relative standard deviations lower than 6%. Limits of detection (LOD) of the optimized FPIA was 0.6 μg/kg for rye and rye crispbread, respectively. Good correlations (r > 0.977) were observed between OTA contents in contaminated samples obtained by FPIA and high-performance liquid chromatography (HPLC) with immunoaffinity cleanup used as reference method. Furthermore, single laboratory validation and small-scale collaborative trials were carried out for the determination of OTA in rye according to Regulation 519/2014/EU laying down procedures for the validation of screening methods. The precision profile of the method, cut-off level and rate of false suspect results confirm the satisfactory analytical performances of assay as a screening method. These findings show that the optimized FPIA is suitable for high-throughput screening, and permits reliable quantitative determination of OTA in rye and rye crispbread at levels that fall below the EU regulatory limits.A rapid fluorescence polarization immunoassay (FPIA) was optimized and validated for the determination of ochratoxin A (OTA) in rye and rye crispbread. Samples were extracted with a mixture of acetonitrile/water (60:40,  Penicillium and Aspergillus species growing in different agricultural commodities in the field or during storage can produce ochratoxin A (OTA) , where A1 and A2 represent the initial  and the final value , respectively, x0 the centre (inflection point), and P the power. The parallelism and position statistical tests were used to compare linear regression curves [p-value = 0.05) were applied in order to assess the homogeneities of variances and means respectively, among the spiking levels of contamination (n = 3). The statistical assessment of results from the single laboratory validation experiments was done by nested ANOVA  using the software package MINITABTM Statistical Software for Windows, version 14 . The rate of false suspect results corresponding to the calculated t-value with one-tailed distribution was calculated using Student’s T Distribution (sheet function “TDIST”).Sigmoidal fits of FPIA data were achieved by means of the unweighted least-square method using Origin version 6.0 . The sigmoidal fit used the logistic equation y = An curves . For rec


One of the major challenges in mental medical care is finding out new instruments for an accurate and objective evaluation of the attention deficit hyperactivity disorder (ADHD). Early ADHD identification, severity assessment, and prompt treatment are essential to avoid the negative effects associated with this mental condition.The aim of our study was to develop a novel ADHD assessment instrument based on Microsoft Kinect, which identifies ADHD cardinal symptoms in order to provide a more accurate evaluation.A group of 30 children, aged 8-12 years , who were referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital , were included in this study. Children were required to meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria of ADHD diagnosis. One of the parents or guardians of the children filled the Spanish version of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN) rating scale used in clinical practice. Each child conducted a Kinect-based continuous performance test (CPT) in which the reaction time (RT), the commission errors, and the time required to complete the reaction (CT) were calculated. The correlations of the 3 predictors, obtained using Kinect methodology, with respect to the scores of the SWAN scale were calculated.The RT achieved a correlation of -.11, -.29, and -.37 with respect to the inattention, hyperactivity, and impulsivity factors of the SWAN scale. The correlations of the commission error with respect to these 3 factors were -.03, .01, and .24, respectively.Our findings show a relation between the Microsoft Kinect-based version of the CPT and ADHD symptomatology assessed through parental report. Results point out the importance of future research on the development of objective measures for the diagnosis of ADHD among children and adolescents. Attention deficit hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder with an estimated prevalence of around 6% . ADHD isEarly ADHD diagnosis is of paramount importance in order to minimize the negative cognitive, emotional, academic, and social effects associated with this condition and to restore the child’s functioning. However, obtaining an accurate ADHD diagnosis is complicated, as most of the psychiatric and psychological criteria are based on clinical evidence. Questionnaires and scales are probably the most common assessment instruments. The Strengths and Weaknesses of ADHD symptoms and Normal behavior (SWAN) rating scale , the diaIn order to alleviate these weaknesses, new instruments have been developed for assessing ADHD. Currently, the continuous performance test (CPT) is one of the most popular tools for evaluating ADHD . In thisIn order to increase the accuracy of the CPT, it has been proposed to additionally collect predictors of the participant’s bodily movements during the execution of the test. One common approach consists of the use of accelerometer-based devices  ,20. HoweA second group of techniques is based on the use of infrared motion trackers , small reflectors attached to the child’s body for the duration of the test to aid ADHD assessment ,23. StudOne of the devices that are attracting more attention is the Microsoft Kinect. It consists of a standard RGB camera, an infrared sensor, an infrared projector, and a set of 4 microphones. Since its appearance in 2010, several studies have attempted its use for cognitive assessment. For instance, Qiu and Helbig found a statistically significant relation between the corporal posture captured with the Kinect camera and the mental load required to complete 4 different tasks . They obIn this paper, we proposed a new method for evaluating the severity of ADHD. The method builds in a previous study by Delgado-Gomez et al , where tThirty children, aged 8-12 years , who were referred to the Child and Adolescent Psychiatry Unit of the Department of Psychiatry at Fundación Jiménez Díaz Hospital , were included in this study. All participants met the DSM-5 criteria for ADHD [The SWAN scale  is the dIn this study, we use a version of the Microsoft Kinect-based CPT proposed by Delgado-Gomez et al . OriginaThe version used in this study includes some modifications to avoid the inconveniences reported by the authors. Namely, the duration of the original Kinect-based CPT resulted exhausting for the participants. Moreover, the gap of 1 s between some stimuli was too short for the examinee to be able to complete the action before the appearance of the next character. In order to tackle these inconveniences, the version used in this study takes only 3 min during which 60 letters are presented. Moreover, the time separation between stimuli is modified to 2, 3, or 5 s. A subset of 12 characters is X-stimuli and the remaining 48 are non–X-stimuli.The RT for the non–X-stimuli, measured as the time elapse between the appearance of the stimulus and the moment in which the participant starts to raise the dominant hand, is calculated. A commissions’ index was calculated for the X-stimuli according to Delgado-Gomez et al. Concisely, it is computed as the ratio of the length of the hand displacement during the X-stimulus and the maximum displacement after the appearance of the previous and posterior stimuli. In addition to these 2 measures, the time required for completing the action of rising and returning the hand to the rest position is also calculated for the non–X-stimuli. Each participant is characterized with the median of the 48 RTs and 48 CTs and the median of the 12 commission indices. The proposed technique is illustrated in the Parents or guardians were required to sign an informed consent after been explained the project in detail. Participants provided assent. The consent and assent forms and the study protocol were reviewed and approved by the Institutional Review Board of Fundación Jiménez Díaz Hospital. All procedures performed were conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.During the experiment, a trained psychiatrist accompanied each of the 10 patients while they conducted the Kinect-based CPT. While each child was performing the Kinect-based test, the corresponding parent or guardian filled the SWAN scale.Before presenting the main results of the experiment, we present the complete performances of 2 examinees in Regarding the commission, the second row in In this paper, we propose an innovative approach to assess the severity of ADHD. With this aim, we have developed a completely Kinect-based version of the CPT that allows tracking the complete movement of the examinees’ dominant hand, instead of only the pulse of the spacebar in the keyboard, during the performance of the test. With this tool, the examiner can not only record commissions, but also detect inhibited movements and obtain a more accurate measure of the reaction times. It is important to mention that the proposed technique differs from other available visually aided approaches, in that they still rely on the keyboard pulsation, whereas in our methodology the individual’s hand takes the place of the spacebar.In particular, our technique computes the RT and the commission indices for each participant. In order to assess their accuracy, these 2 predictors are compared with each of the 3 factors  of the SWAN scale. The results show that the proposed approach is capable of assessing hyperactivity and impulsivity degrees of individuals diagnosed with ADHD. Regarding the commissions, notwithstanding that the correlation with impulsivity is larger than .2, this value is not statistically significant. A possible explanation for this, as suggested by the much larger values observed in a previous work conducted with healthy population, is that the increase in the inter-stimuli time allows the patient to take a more relaxed approach to the test as then mental load is reduced. Future work will consider reducing these times. These results are in concordance with the ones reported in the literature: in particular, (1) there is a positive correlation between impulsivity and the number of commissions ; and (2)Our results have also allowed us to establish a correlation between the RT and impulsivity or hyperactivity. This result is also consistent with the available literature. Moreover, this is an interesting result as Li et al  did not Although our results have shown the proposed approach as a promising research line, its main limitation is the small sample size, consequence of the difficulty of enrolling participants with specific characteristics (children with the age of 8 to 12 years diagnosed with ADHD). Therefore, it is the intention of the authors to replicate the experiment with a larger number of individuals, where the different ADHD subgroups are well represented. Despite the reduced sample size, our results point out our proposed methodology as an alternative for overcoming the limitations of the current ADHD assessment instruments based mainly on scales and questionnaires. From the clinical prospective, it can be an important aid in differentiating ADHD subtypes. It could also be useful for evaluating the impact of parent’s  perception of the child’s symptoms on the scales results . Moreove


Trypanosoma cruzi to mammals, including humans, through their feces. This parasitic protozoan is the causative agent of Chagas disease, a major public health issue in endemic areas. Because of the medical and economic impact of Chagas disease, the presence of other arthropod-borne pathogens in triatomines was rarely investigated.Among the Reduviidae family, triatomines are giant blood-sucking bugs. They are well known in Central and South America where they transmit T. cruzi were molecularly screened for the presence of known pathogens generally associated with arthropods, such as Rickettsia, Bartonella, Anaplasmataceae, Borrelia species and Coxiella burnetii. Of all included triatomine species, only Eratyrus mucronatus specimens tested positive for Bartonella species for 56% of tested samples. A new genotype of Bartonella spp. was detected in 13/23 Eratyrus mucronatus specimens, an important vector of T. cruzi to humans. This bacterium was further characterized by sequencing fragments of the ftsZ, gltA and rpoB genes. Depending on the targeted gene, this agent shares 84% to 91% of identity with B. bacilliformis, the agent of Carrion’s disease, a deadly sandfly-borne infectious disease endemic in South America. It is also closely related to animal pathogens such as B. bovis and B. chomelii.In this study, seven triatomines species involved in the transmission of E. mucronatus is an invasive species that occasionally feeds on humans, the presence of potentially pathogenic Bartonella-infected bugs could present another risk for human health, along with the T. cruzi issue.As  T. cruzi, the agent of Chagas disease, a huge public health issue, especially in South America. Whether these arthropods carry other pathogenic microorganisms is currently unknown. We investigated the presence of different arthropod-borne pathogens, including Bartonella spp., by quantitative PCR. Bartonella species were identified using ftsZ, gltA and rpoB gene sequencing and a new genotype of Bartonella spp. was detected in Eratyrus mucronatus specimens, an important vector of T. cruzi to humans. This agent is closely related to several human and animal pathogens. Depending on the gene fragment used, this agent shares 84% to 91% of identity with B. bacilliformis, the agent of the deadly Carrion’s disease. The possibility of transmission of potentially pathogenic bacteria could be an additional threat to human health since E. mucronatus bugs are more and more anthropophilic.Triatomines are hematophagous insects including vectors of  T. cruzi, the etiological agent of Chagas disease [T. cruzi is transmitted through the feces of infected kissing bugs, causing heart failure 10 to 30 years post-infection for almost 30% of individuals [Triatomine bugs  are blood-sucking arthropods (“kissing bugs”), most of which can feed both on animals and humans. All stages from first instar to male and female adults are strictly hematophagous and responsible for a relatively large blood intake due to their large size. They are mainly sylvatic and feed on small wild mammals but can also feed on birds and bats . Triatom disease . Also knividuals .Wolbachia and Arsenophonus species was investigated based on the fact that these obligate intracellular bacteria are known to be endosymbionts of many arthropods [T. cruzi, although Arsenophonus nasoniae was once reported to be detected in an eschar of a human [Triatoma virus is reported as strictly entomopathogenic, particularly for its principal host, Triatoma infestans [African swine fever virus was detected in Triatoma gerstaeckeri but not transmitted to pigs [Because of the public health impact of Chagas disease, studies on kissing bugs are mainly focused on this theme. As a matter of fact, the presence of other human pathogens was never described in the hundred years that it has been known that kissing bugs could transmit pathogens. Only the presence of thropods ,6. The p a human . Regardinfestans , while A to pigs .2 overseas department and region of France bordered by Brazil and Suriname. Because of its many different ecosystems, particularly a dense rainforest, French Guiana is a biodiversity hotspot and one of the 21 areas where Chagas disease is endemic [Panstrongylus, Rhodnius and Eratyrus genera [French Guiana is an 84,000 km endemic . Among ts genera . They acs genera  and alsoRickettsia, Bartonella, Borrelia, Anaplasma, Wolbachia, Ehrlichia species and Coxiella burnetii.Aiming to add to knowledge regarding bacteria and triatomine association, we screened seven species of triatomines bugs from French Guiana by molecular biology for the presence of arthropod-borne bacteria such as Triatomine specimens were collected in French Guiana from 1991 to 2013 using light traps or interception traps by one of the authors (JMB) and by the Société Entomologique Antilles-Guyane (SEAG) as part of an inventory of French Guiana’s insects. Triatomines were caught in forests , peridomiciliary areas  or urban areas  as displayed in T.cruzi vectors were included in this study: Rhodnius prolixus (n = 10), Rh. pictipes (n = 10), Rh. robustus (n = 10), Triatoma infestans (n = 10), Panstrongylus geniculatus (n = 10), P. rufotuberculatus (n = 4), Eratyrus mucronatus (n = 23).All triatomine specimens were morphological identified with the Bérenger et al. morphological key  and keptTriatominae genomic DNA was individually extracted using the EZ1 DNA tissue extraction kit  according to the manufacturer’s instructions. Triatominae DNAs were then eluted in 100 μL of Tris EDTA (TE) buffer using the DNA extracting EZ1 Advanced XL Robot (Qiagen) as previously described [Dried triatomines were rinsed in sterile water and air-dried on filter paper before cutting lengthwise in two equal halves, using a sterile surgical blade for each specimen. One half and the legs were stored at -20°C as a backup sample and the other legless half was selected for molecular analyses. Each half triatomine was crushed with a sterile pestle in 400 μL of a G2 buffer solution containing 40 μM of proteinase K (Qiagen) and incubated at 56°C overnight. After 1 minute of centrifugation at 7000 x g, 200 μL of the supernatant was then collected prior to DNA extraction. escribed . DNAs weBartonella spp., but also Rickettsia spp., Coxiella burnetii, Borrelia spp., and all Anaplasmataceae species [DNA samples were individually tested by genus-specific PCR using primers and probes targeting specific sequences of  species  as previ species   gene was performed on PCR negative triatomines to control DNA extraction.DNAs were tested at different concentrations to avoid PCR inhibition. For each run, a PCR mix without DNA was used as negative control. Standard PCR targeting a 710 bp region of the invertebrate Bartonella-qPCR were submitted to conventional PCR amplification using a Bio-Rad Thermocycler  prior to sequencing. For Bartonella species identification, primers targeting Bartonella rpoB, gltA and ftsZ genes fragments were used as previously described [Bartonella elizabethae served as PCR positive control and mixture without DNA as negative control. The cycling protocol consisted of 15 min at 95°C followed by 35 cycles of denaturing at 95°C for 30 s, annealing at 50°C for 30 s (58°C for rpoB gene), extension 1 min at 72°C, followed by a final cycle of 1 min at 72°C and sampling held at 4°C. Amplification products were separated by electrophoresis through a 1.5% agarose-tris-borate-EDTA gel containing ethidium bromide. PCR products were sequenced using a Big Dye Terminator kit and an ABI PRISM 3130 Genetic Analyser . The sequences were analyzed using the ABI PRISM DNA Sequencing Analysis software version 3.0 (Applied BioSystems) and compared to sequences available in the GenBank database using the BLAST algorithm (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The partial sequences of ftsZ and rpoB genes of Bartonella amplified from the sample EmG01 are available in GenBank at #KX377404 and #KX377405.DNA samples that were positive with escribed . DNA froBartonella with other members of the Bartonella genus was established with TOPALi 2.5 software . Available sequences of ftsZ, gltA and rpoB genes of validated Bartonella species were retrieved from the National Center for Biotechnology Information (NCBI) based on the results of the BLAST program. Multiple sequence alignment was performed with the ClustalW multiple sequence alignment program, which is included in the BioEdit software.Phylogeny of the detected Eratyrus mucronatus  were male. Further details regarding other collected species have been listed elsewhere [Triatomines were collected in eight different localities in French Guiana with no selection regarding species and sex (convenient sampling). Among the triatomines collected, cronatus  accountelsewhere .Bartonella species. Of 23 Eratyrus mucronatus samples, 13 (56.5%) were positive by Bartonella spp.-specific qPCR, with cycle threshold (Ct) values ranging from 13.23 to 25.91 (mean: 21.44)  . These sRickettsia spp., Borrelia spp. Anaplasma spp., Ehrlichia spp., Wolbachia spp. and Coxiella burnetii. Bartonella spp. was only detected in Eratyrus mucronatus specimens.All samples tested negative for the presence of Bartonella spp. rpoB gene was amplified using conventional PCR primers prior to sequencing. Only three ITS2-qPCR positive samples were also positive for rpoB by standard PCR. Sequencing failed for two of them. Comparison of the one rpoB resulting sequence against the NCBI database using the BLASTN program indicated that it possessed 90% identity with the ATCC Bartonella bacilliformis 35685D-5 strain (#CP014012.1) and with the B. bacilliformis KC583 strain (#CP000524.1). The next closest cultivated strains were a B. bovis strain [B. chomelii strain [Bartonella ancashensis strain [Bartonella rpoB gene were retrieved from NCBI and compared to the Bartonella sequence described hereby. This Bartonella genotype formed a distinct clade, with a strain of Bartonella bacilliformis as the closest clade based on rpoB gene analysis , human body lice (Pediculus humanus humanus), different fleas including cat fleas , biting flies and ticks [Bartonella species, some have been identified as human pathogens, causing well-known vector-borne diseases such as Carrion’s disease (B. bacilliformis), trench fever (B. quintana), cat scratch disease (B. henselae) as well as endocarditis [g humans . They arnd ticks . Among tcarditis .Bartonella genotype, phylogenetically related to several human and animal pathogens, as shown by the phylogenetic analyses. B. bacilliformis, a closely related species, is the causative agent of the first and well-described human bartonellosis called Carrion’s disease [L. verrucarum, this South American endemic bacterium can induce a biphasic illness with two distinct syndromes that can be concomitant or independent. An acute phase known as Oroya fever manifests as a hemolytic fever linked to bacteremia that can range from 10 to 210 days and can be fatal in 40–88% of individuals without treatment. The second syndrome called verruga peruana manifests as blood-filled hemangiomas due to infection of the endothelium [B. bacilliformis infection have been reported in French Guiana to date [B. ancashensis, a recently described Bartonella species closely related to B. bacilliformis that was isolated from the blood of two patients diagnosed with a chronic stage of verruga peruana in Peru [B. ancashensis could be a second agent. Our new agent is also closely related to B. bovis strains. Isolated from cats, which are only accidental hosts, this endocarditis [We hereby describe a novel  disease . Transmiothelium . No huma to date . Our gen in Peru . All datcarditis .E. mucronatus is a sylvatic triatominae bug involved in the transmission of T. cruzi [Bartonella spp. worldwide, including in French Guiana [Bartonella spp. were frequently detected in hematophagous arthropods feeding on bats such as bat flies  [Cimex adjunctus [Triatoma, Rhodnius, Panstrongylus and Eratyrus can be totally domiciliated or invasive, since they occasionally visit houses as described in Bolivia [E. mucronatus has not yet been described in French Guiana but data from Bolivia suggest that eliminating it once it is settled is challenging [T. cruzi . It is rT. cruzi  because T. cruzi . They arT. cruzi . Bats arT. cruzi , includih Guiana , Nigeriah Guiana , Guatemah Guiana  and Vieth Guiana . Therefoibiidae)  or Cimexdjunctus . Triatom Bolivia , Brazil  Bolivia , Argenti Bolivia  and Vene Bolivia . The pre Bolivia . The invllenging . Living llenging , these bllenging , both ofa priori endosymbiotic. The specimens we analyzed were dry, with no information regarding their engorgement status at the time of collection. However, as they were collected using light traps or interception traps, we can assume that they were seeking hosts and therefore probably non-engorged. Thus, we can suppose that we did not detect DNA of a bacterium present in recently ingested blood but a genuine infection. To support this hypothesis, the infection rate was considerable (56%) among triatomines collected in very distant sampling periods, geographically and over time. Ct values were also very low, increasing the possibilities that this bacterium multiplies within the bug's body. However, to evaluate the possibility of transmission of these Bartonella spp., an experimental model of infection, or at least information regarding the location of the bacteria in the bug, would be necessary. Such information could not be obtained from our samples as they were dry and old. Cultivation of any bacteria or any attempt to localize with immunofluorescence, for example, was not possible.Triatominae species are well-studied bugs, however, this work provides the first evidence to our knowledge of infection with a bacterium that is not E. mucronatus specimens in order to isolate the bacterium and establish an experimental model of infection with this arthropod/pathogen pair. This would reveal whether the bug is a simple carrier or an efficient vector of this Bartonella. The possible interaction between T. cruzi and this Bartonella spp. in this insect is also unknown and could be investigated by monitoring the trypanosome’s cycle and transmission in co-infected E. mucronatus. Being phylogenetically closely related to two severe human pathogens (B. bacilliformis and B. ancashensis), it would also be important to evaluate its pathogenicity. Because of the huge public health impact of Chagas disease in South America, investigations on Triatominae were limited to the study of their interactions with T. cruzi. In fact, Triatominae bugs may host such bacteria as Bartonella species and, probably, may be its vector.In continuing this work, it would be interesting to collect wild


SD = 2.7) predicted ADHD symptom severity and overall functioning 6 years later  in a carefully phenotyped large sample of 226 Caucasian participants from 182 families diagnosed with ADHD-combined type. Outcome measures were dimensional measures of ADHD symptom severity and the Kiddie-Global Assessment Scale (K-GAS) for overall functioning. Predictors were derived from component scores for 8 domains of neurocognitive functioning: working memory, motor inhibition, cognitive inhibition, reaction time variability, timing, information processing speed, motor control, intelligence. Effects of age, gender, and pharmacological treatment were considered. Results showed that better working memory predicted lower ADHD symptom severity (R2 = 3.0 %), and less reaction time variability predicted better overall functioning . Predictors were still significant with baseline behavior included in the models. The role of neurocognitive functioning in the long term outcome of ADHD behavior is discussed.Although a broad array of neurocognitive dysfunctions are associated with ADHD, it is unknown whether these dysfunctions play a role in the course of ADHD symptoms. The present longitudinal study investigated whether neurocognitive functions assessed at study-entry  contains supplementary material, which is available to authorized users. Attention-Deficit/Hyperactivity Disorder (ADHD) is a common developmental disorder, with impairing and highly persistent symptoms of inattention and/or hyperactivity/impulsivity , rather than on more sensitive continuous measures of symptom severity  ADHD symptom severity and (2) overall functioning, using a longitudinal design with a 6-year follow-up of children with combined-type ADHD (ADHD/C). We investigated children in the full range of childhood age, with careful consideration of age-dependent effects. As ADHD is associated with heterogeneity in neurocognitive deficits  with a DSM-IV-TR diagnosis of ADHD/C aged 5–19 years were recruited from outpatient clinics and via advertisements between 2003 and 2006 in the Dutch part of the International Multicenter ADHD Genetics (IMAGE) study. Six years later, subjects were invited for a comprehensive follow-up assessment as part of the NeuroIMAGE study  and 347 participants (75.6 %) were retained successfully. Of these 347 participants, 226 participants participated in the neuropsychological assessment during the IMAGE study (baseline measurement) and were included in the current study. Results of attrition analyses are described in the Results section.Participants , M (DSM-IV Hyperactive/impulsive symptoms), and N , and scores ≥90th percentile on the SDQ Hyperactivity subscale were considered clinical. Participants obtaining clinical scores on any of these subscales were administered the Parental Account of Children’s Symptoms (PACS), a semi-structured, standardized, investigator-based interview with the parents as informants (Taylor SD = 2.7), and 17.4 years (SD = 2.7) at follow-up, and 82.6 % was male. Regarding medication use at follow-up, 88.5 % of all participants used stimulants, 11.9 % of all participants used atomoxetine, 4.9 % of all participants used antidepressants, and 1.3 % of all participants used tranquillizers .Selection and diagnostic procedures at baseline have been detailed previously  fs Taylor . The 226At follow-up, ADHD total symptom severity as well as inattentive and hyperactive/impulsive symptom severity were assessed with the Conners’ Parent Rating Scale–Revised: Long version  of the Dutch version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime Version  or JB at the Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Centre, Nijmegen (K-SADS). Trained interviewers used the same training and supervision procedures for additional interviewers at the participating centers. Inter-rater agreement for the PACS was 0.88 , cognitive inhibition (the ability to flexibly shift between two response options), reaction time variability, timing, information processing speed, motor control, and intelligence. Domains were chosen to include a broad array of neurocognitive tasks (see Table Table 2 (assessed at baseline), gender, pharmacological treatment until follow-up and study site (Amsterdam or Nijmegen) were added as covariates to the final models. Pharmacological treatment was collected in terms of the cumulative intake of psychostimulants (mean daily dose multiplied with treatment duration corrected for age) from age of onset until the follow-up assessment using information from pharmacy records supplemented with information from parent questionnaires. See van Lieshout et al. . Likewise, baseline impairment was included when predicting overall functioning. Impairment at baseline was measured by the impairment scale of the Strengths and Difficulties Questionnaires . Percentage of missing data at follow-up was 2.2 % for ADHD symptom severity measures, and 3.1 % for overall functioning measures. All outcome variables had a normal distribution with values of skewness and kurtosis within the range of −1 to +1, except for the K-GAS score (kurtosis = −1.20). K-GAS-scores were normalized by applying a Van der Waerden transformation.In the sample of 226 children, the Stop task was not administered to 9.7 % of participants due to technical problems. Between 0.4–3.1 % of data were missing for other neurocognitive predictors. Missing value analysis (Expectation Maximization with 25 iterations) for Stop Task data was performed only for participants with at least nine out of ten neurocognitive tasks available (p > 0.05), until an optimal final model was composed. The mean correlation between predictors was 0.36 (0.03 ≤ r ≤ 0.70), indicating no collinearity. Analyses predicting symptom severity or impairment used three steps: (1) Models predicting symptom severity or impairment were calculated with the neurocognitive predictors only (model 1). (2) To investigate the additional predictive value of neurocognitive functioning over and above baseline behavior or baseline impairment, models were calculated with the neurocognitive predictors together with baseline symptom severity or baseline impairment (model 2). (3) Then, models were calculated with baseline symptom severity or baseline impairment only, for comparison (model 3). We described differences between the models (1) and (2), by evaluating the difference in R2.To optimally correct for the familial dependency in our data, Generalized Estimating Equation analyses (GEE) were used with an exchangeable correlation structure. An optimal set of predictors for (1) symptom severity and (2) overall functioning was derived by performing a backward selection procedure . The same procedure was followed for gender. The final model of ADHD symptom severity (model 1) was reran using both inattentive and hyperactive/impulsive symptoms as outcome measures (including baseline symptom severity), to explore whether the model was applicable to both symptom axes.To investigate age effects, both age and quadratic effects of age were examined. Interactions between age and significant predictors of outcome were added to the final models with neurocognitive functioning (model 1). When an interaction-effect with age or age2, gender, pharmacological treatment, study site) on the final models.As the reliability and validity of the CPRS-R:L is only established for children under 18 years of age, we checked whether results of the final model for ADHD symptom severity (model 1) were robust when tested in children younger than 18 years. Beside possible moderating effects of age and gender, we tested possible effects of confounders  with participants lost to follow-up from the total sample on 25 variables available at baseline . Participants who were lost to follow-up had higher SD (corrected for MRT) on the motor timing task (p < 0.001) and had more commission errors on the Stop task (p = 0.036). No other significant group differences were found (0.070 < p < 0.983).Attrition was investigated by comparing participants successfully followed up . Table Follow-up interval was not related to current ADHD symptom severity  and therefore included as a covariate in all further analyses. Table Follow-up interval was related to the K-GAS-score , age2 , or gender .Significant neurocognitive predictors for both ADHD symptom severity (working memory) and overall functioning (reaction time variability) did not significantly interact with age (p = 0.174), but was a significant predictor for current ADHD hyperactivity/impulsivity symptom severity; better working memory pre-dicted lower hyperactivity/impulsivity symptom severity (p = 0.001). R2 for the final model of hyperactivity/impulsivity was 3.91 %.Working memory (model 1) was not a significant predictor for ADHD inattention symptom severity . Taking into account that the sample size of this subsample was substantially smaller, these findings suggest that the results also hold when the age group was excluded for which the CPRS-R:L was not validated.Predictors relevant in the final model (model 1) for current ADHD symptom severity were significant with similar relationships when tested in a subsample of children younger than 18 years . Covariates were not significant in the final models for ADHD symptom severity and overall functioning (0.068 < p < 0.830).Findings for current ADHD symptom severity as well as for overall functioning replicated when age or ageThe current large prospective study investigated whether a broad array of well-defined neurocognitive measures predicted dimensional outcome measures of ADHD, 6 years later, in children and adolescents with ADHD-combined type. In summary, better working memory predicted lower symptom severity 6 years later, and less reaction time variability predicted better overall functioning. Percentage of explained variance for the neurocognitive predictors was small (3.0–5.6 %) but significant. Together with baseline behavior, neurocognitive predictors remained significant, and a higher percentage of variance was explained compared to the percentage of variance explained by the baseline behavioral measures or neurocognitive measures alone.The current finding that both better working memory and less reaction time variability contribute to better outcomes is in line with our hypothesis that better early neurocognitive functioning is associated with a positive outcome of ADHD. Interestingly, our finding is also consistent with the results of a recent study in which ADHD symptoms were assessed on a continuum both at baseline and follow-up, showing that better working memory and less reaction time variability predicted future ADHD symptoms and academic achievement, over and above baseline behavioral symptoms , which would explain that we did not find a longitudinal relationship between most of our neurocognitive measures and overall functioning in particular.In this study, we addressed questions regarding age, gender, pharmacological treatment, follow-up interval and study site, to contribute to existing literature. Previous studies revealed mainly negative findings on the predictive value of early neurocognitive functions for ADHD outcomes. As brain development is thought to be non-linear  functioning at the current moment, or to help understand and explain certain behavioral problems or impairments.Some limitations should be noted. First, some aspects of our sample limit generalization to the population, including our exclusive focus on participants with the combined type of ADHD  predicts better outcome. Our findings challenge the role of neurocognitive functioning in the long term outcome of ADHD.Table S1Supplement (DOCX 18 kb)