ADHD symptoms were measured using a DSM-IV based subscale from the Child Behavior Checklist. Children with diagnosed ADHD originated from the clinic-referred cohort. Road traffic noise (Lden) was estimated at the residence and school level, by model calculation. Risk ratios for ADHD symptoms and ADHD diagnoses, and regression coefficients for symptom severity were estimated separately and simultaneously for residential and school road traffic noise. Adjusted multinomial models with residential road traffic noise showed that residential noise was not associated with ADHD symptoms, but was associated with lower risks for ADHD diagnosis . Similar associations were observed for models including school road traffic noise and models including both exposures. No clear exposure response relationship was observed for associations between residential or school noise and ADHD symptom severity. We found no evidence for a harmful association between road traffic noise and ADHD. Associations between noise and lower risks for ADHD were observed only in referred cases with a confirmed ADHD diagnosis and may be due to residual confounding or selection bias. Future studies should focus on residential and school noise exposure, and study associations with ADHD symptoms and diagnosis over time.Environmental noise may play a role in the manifestation and severity of attention deficit/hyperactivity disorder (ADHD) symptoms, but evidence is limited. We investigated the cross-sectional associations between residential and school road traffic noise exposure and ADHD symptoms and diagnosis. The sample included The online version of this article (10.1007/s00787-020-01521-8) contains supplementary material, which is available to authorized users. Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with symptoms continuing into adulthood , 2. ADHDAlthough the exact causes of the disorder have not yet been identified, it is evident that both biological as well as environmental factors are accountable for the manifestation of the disorder. Research on biological factors primarily involves genetics and brain structure and function , 5. ADHDn = 872) that reported that higher road traffic noise levels were associated with more hyperactivity/inattention symptoms [n = 1384) from Oslo, Norway [n = 2897) focused on traffic noise at schools, and found that noise exposure at the school was associated with ADHD symptoms [Studies have shown associations between environmental noise and ADHD symptoms. A large cohort study performed in 46,940 children from Denmark found that a 10 decibel increase in road traffic noise exposure from birth to age 7 was associated with a 9% increase in borderline and abnormal hyperactivity/inattention subscale scores [symptoms , 12.Other previous studies focused on adverse effects of noise on children’s cognition. Particularly, tasks relying on sustained attention  were found to deteriorate as a result of exposure to traffic noise , 13–17, Environmental noise may play a role in the manifestation and severity of ADHD symptoms, but evidence is limited. Most of the previous research solely assessed ADHD symptoms and not clinical diagnosis. They further focused on residential road traffic noise alone, while children spend a large part of their day at school. To address these gaps, we investigated the associations between residential and school road traffic noise exposure (separately and simultaneously) and ADHD symptoms and diagnosis.n = 2230) and clinic-referred (n = 543) cohorts of TRAILS . Details about this cohort and the recruitment of participants have been published previously [Participants were a subsample from the population  at age 11 from the TRAILS clinical cohort. In the population cohort, we identified a subsample who screened positive for possible ADHD, and randomly drew a gender-matched reference sample from those who screened negative . See Fig. For the present study, we sampled children with a lifetime diagnosis of ADHD and currently elevated ADHD symptoms . The study was carried out in accordance to the Declaration of Helsinki and all measurements were carried out with their adequate understanding and written consent.In the TRAILS clinical cohort, information on the presence or absence of ADHD was assessed with the Diagnostic Interview Schedule for Children (DISC-IV parent version) . In addin = 1222 was selected and added to the population cohort study sample. Finally, 121 individuals were excluded due to missing data on traffic noise exposure or covariates yielding a final sample of n = 1710  of Lden. Lden is the average 24 h sound level, with a 10 dB penalty added to the levels between 23.00 and 07.00 h and a 5 dB penalty added to the levels between 19.00 and 23.00 h to reflect people’s extra sensitivity to noise during the night and the evening. Road traffic noise exposure was calculated at 4 m above the ground of the dwelling facade of the exposed subject [Road traffic noise was calculated at the residence and school levels. Calculations were done at the most exposed facade of the building using the STAMINA (Standard Model Instrumentation for Noise Assessments) in accordance with the requirements of the European Environmental Noise Directive (END). The road traffic noise model was based on 2011 data, and included information on traffic intensity, speed, composition, type of road surface, building data, and surface type. We used the EU standard noise metric  subject .z-scores were computed reflecting parental internalizing problems (anxiety and depression) and parental externalizing problems  [Unpleasant indoor environment .Dirty indoor environment .Comfortable home .Spacious, pleasant outdoor environment .The covariates were chosen a priori based on the previous studies –10. Datae abuse) . FinallyAll items were answered on a 5-point scale ranging from strongly disagree to strongly agree. The four questions were normalized and scores were summed into a quality score ranging from 4 to 20, with higher scores indicating higher quality of the residential environment.For noise sensitivity, children answered the following question: “I notice sounds around me before other people do ”, on a 5-point scale ranging from (1) “almost never true”, to (5) “almost always true”.Lden and school Lden as follows:Lden) and ADHD with multinomial regression analyses. Participants were classified into three groups: no ADHD symptoms (reference group); ADHD symptoms in population cohort; and ADHD diagnosis in clinical cohort. Models were stepwise adjusted for: (1) age and sex; (2) parental SES, single parenthood, parents born outside The Netherlands; (3) maternal smoking during pregnancy, maternal alcohol use during pregnancy, problems during pregnancy or delivery; and  parental internalizing and externalizing problems. Associations were estimated separately for residential and school road traffic noise, but also simultaneously including noise estimated for the residence and school.Multinomial regression analyses were used to estimate associations between residential and school road traffic noise .Linear regression analyses were undertaken with ADHD symptom severity as outcome. This was done (i) in the total sample; (ii) in a sample with those with ADHD symptoms and ADHD diagnosis; and (iii) in only those with an ADHD diagnosis. We furthermore performed linear regression analyses with symptom severity scores for two ADHD subscales: attention and hyperactivity/impulsivity in these groups. In addition to analyses with continuous Lden and ADHD symptoms and diagnosis.Multinomial regression analyses (see above at 1.) stratified for sex, parental SES (stratification based on median), and parental internalizing and externalizing problems (stratification based on median), adjusted for the same set of covariates as specified above  were undertaken to evaluate sex, SES, and parental psychopathology differences in the relationship between Descriptive statistics were used to characterize the study population, and were calculated for the pooled sample and for those without ADHD, those with ADHD symptoms, and those with an ADHD diagnosis as described above. Equivalence tests  and were used to test for differences between groups. Risk ratios and regression coefficients for ADHD were estimated for residential For the first set of analyses (see above at 1.), we performed sensitivity analyses by applying a more stringent cut-off for ADHD symptoms in the population cohort and defined an ADHD score above the 95th percentile of the CBCL or the short form of the TRF as indicative of the presence of ADHD problems . We furtn = 1710 for residential and n = 1538 for school Lden analyses). Effect estimates are presented as risk ratios  or regression coefficients , or odds ratios  with 95% confidence intervals (CI), per 1 dB(A) or categories of Lden. Associations were considered statistically significant if the 95% confidence intervals did not include one (RR) or zero (β). Analyses were performed using the statistical software package STATA version 14.2 [Data were analyzed for participants from whom we had complete data (ion 14.2 .r = 0.012), and neither were noise and parental SES .Population characteristics of the total sample and by ADHD status are presented in Table Multinomial regression analyses showed that higher residential road traffic noise levels were associated with lower risks for ADHD diagnosis, but not for ADHD symptoms. The association between noise and ADHD diagnosis remained statistically significant in the fully adjusted model  had on average − 0.135  lower scores for attention symptom severity  for ADHD symptoms did not result in different results: higher noise levels were associated with lower risks for a clinical ADHD diagnosis, but not ADHD symptoms. Although risk ratios for ADHD symptoms in relation to residential road traffic noise became > 1, these were not statistically significant Table . AdditioFinally, additional adjustment for residential environment quality in the population sample did not change the results .In this cross-sectional study of 1710 children, we found no evidence for a harmful association between road traffic noise and ADHD. Higher noise levels at the residence and school were associated with a lower risk of a clinical ADHD diagnosis, but not with ADHD symptoms in the population cohort or with symptom severity. The results were consistent after adjustment for various known risk factors for ADHD and socioeconomic status and for different cut-offs of ADHD symptoms.Our results differ from the previous studies that did observe harmful associations between road traffic noise and ADHD symptoms –10. AverGiven the results of previous studies –10 and tFinally, the association between road traffic noise and ADHD did not differ by low or high noise sensitive children. This was assessed in the population sample only, and potential effect modification could thus not be studied in the clinical sample. Noise sensitivity refers to the variability in reactivity to different sources of noise in individuals. Although not studied extensively in children, noise sensitivity may be an important factor in the association between noise exposure, annoyance, and behavioral problems , 36, 37.This study was based on a rich data set that enabled us to adjust for potential confounders and perform a number of sensitivity analyses. Children spend a large part of their time at school, and thus, having road traffic noise data for both homes and schools was a major advantage. Limitations include that the period of cohort’s measurements and the year of the noise model are 10 years apart, and may have led to exposure misclassification. Potential exposure misclassification is assumed to be small, assuming that spatial variability in noise exposure remained similar, and that traffic intensity would not have strongly increased. These assumptions are reasonable, because historical changes in infrastructure , and thus in spatial variability, are assumed to be limited, especially in the urban areas of our study area where the largest part of our study population lives . Note fuThis cross-sectional study found no evidence for a harmful association between road traffic noise and ADHD. Longitudinal studies should focus on the association between traffic noise and ADHD in schools and at home.Supplementary file1 (DOCX 15 kb)Below is the link to the electronic supplementary material.


In this study, a novel finishing method, entitled clustered magnetorheological finish (CMRF), was proposed to improve surface finish of the silicon nitride ( Silicon nitride , magnetic abrasive finishing (MAF) and magnetic compound fluid slurry finishing (MCF), are capable of achieving surface roughnesses in the range of 10–100 nm on metals with flexible finishing medium ,12,13. B 5.06 nm . HoweverIt is therefore suggested that CMRF is a promising method for finishing ceramic materials according to those aforementioned studies. However, the application of CMRF on the finish of spherical parts made of ceramics has not been reported yet. In this study, the novel application of the CMRF technique for polishing In this study, the CMRF method was applied to improve the surface finish in the polishing of By driving the rotating shafts of upper and lower finishing plates, the ceramic balls rotated and were polished under the shear force of magnetorheological finishing pads b. DifferA series of polishing experiments were conducted on a customized CNC CMRF machine . The machine tool was modified based on a V-groove finishing machine by assembling a series of circularly-distributed electromagnets was fixed on the two polishing plates, as shown in 3O4), and the volume fractions (%) of different components were listed in The workpiece used in this study was and SiC) ,17. The δ) and the eccentricity of the upper and lower plates (e) are the main parameters which affect the quality of the surface finish, because these parameters affect the tool trace of the polishing. In this study, different δ and e were selected and adopted orthogonally to investigate their effects on the quality of final surface finish. As presented in In the polishing of ceramic balls , the rotation speed of the upper and lower plates (Sph) was measured by a roundness measuring instrument . Results of measurements of surface roughness and sphericity were presented in The time of each polishing experiment was sixty minutes. After CMRF processes, the surface roughness and sphericity were measured, both of which were often used to evaluate the quality of the surface finishing of spherical parts in industry. The surface roughness (Ra) was measured with a a white light interferometer  and the sphericity  when one of the cutting parameters was fixed. For example, the average roughness at the machining gap 0.8 mm was calculated as: Furthermore, significance analysis was conducted to evaluate the influence of different cutting parameters on surface roughness and sphericity. The factor of significance δ), 2.661 (e), 2.57 (From the results listed in Ra of Test 3 and Test 4). On the other hand, the eccentricity of the abrasive plates influenced the surface finish by changing the distribution of the magnetic field. It was reported in the study of Jain et al. [According to the significance analysis, it could be concluded that the finishing parameters employed in this study affected the surface roughness and sphericity in different ways. The machining gaps influenced the final surface finish by affecting the mechanical properties of magnetorheological pad. According to the findings of Pan et al. , the mecn et al.  that theThe optical images and micro morphologies of ceramic balls before and after the CMRF process are compared in The finest surface finishing was found on the surface of No. 9 test a. NonethWhen polishing  surface . This meAccording to the experimental results, it could be suggested that the eccentricity, rotating speeds, and machining gap are three major parameters that affect the surface roughness and sphericity. To investigate the mechanism of the influence of different cutting parameters on the surface finishing, a mathematical model was developed to analyze the kinematic status of the balls at different eccentricities, rotating speeds, and machining gaps in the CMRF of spherical parts. 1x1y1O and 2x2y2O were two coordinates developed in the reference systems of upper plate and lower plate, respectively (1O and 2O are the centers of the plates). In this study, the rotation status of three contact points one at the interface of the workpiece/upper magnetorheological finishing pad (point A) and other two at the interface of the workpiece/upper magnetorheological finishing pad (point B and point C) was modelled. The kinematics of the ceramic ball was investigated by analyzing the rotating speed zω and jω, which were the orthogonal components of and the rotating angle θ and γ  are the angular speed of A, B and C; the and these rotation speeds are corresponded to Aω, Bω, and Cω (Bω = Cω), respectively, The ball billet with a radius br self-rotated at an angular velocity sω, and the shape of the V-groove is determined by the groove half-angle α. rA = VAcosλ, VrA = VAsinλ, VA = R’AωA:V1O2AO:According to the assumption that no relative sliding at the contact point between the ball billets and finishing pad would occur, the kinematics equations can be written as follows:Thist can be solved by Equation (2) to Equation (5):α is fixed, the motion of sphere could be controlled by changing the position radius of the groove AR, the eccentricity e between the two rotating plates, and the rotation speeds Aω and Bω (or Cω). Acordingly, a high efficiency and uniform spherical envelope model could be achieved. In addition, the magnetic field intensity, magnetorheological finishing force, and machining gap δ can be adjusted, then the surface roughness and spherical error of the silicon nitride ceramic balls could be optimized and controlled.Based on Equations (6) and (7), it was found that when the groove half-angle The movement of the three contact points were simulated by using the software ADAMS  to present the visualized kinematic status of the Aω/Bω, and machining gap were selected to simulate and optimize the finishing trajectory by using single variable method.The material properties of Si3N4  and the Si3N4  were preA, B, and C) between the ball billet and the magnetorheological finishing pad can be simulated by ADAMS. The simulation time is set as 30 s with a step size of 0.01, and the function expression of finishing speed is listed in Based on above spherical envelope model and the contact force parameters, the trace of three contact points . Both QS, the better the finishing uniformity of the whole sphere.The positions of the contact points at the time SDs, the simulative results could be compared further. Specifically, the machining gap exhibited the most significant influence on the distribution of the contact points, which was reflected by the 0.5 on the variance of SDs It was found that the surface roughness of (2)The finishing parameters affected the surface roughness and sphericity. The adoption of larger machining gaps increased the surface roughness due to the decrease of the stiffness of the magnetorheological pads. The eccentricity influenced the surface finish by changing the distribution of the magnetic field, and the ratio of the rotating speeds influenced the stability of the CMRF processes.(3)The modelling and simulation of CMRF showed the visualized traces and distributions of the contact points with different polishing parameters. The distribution of contact points in different sections showed that the contact points were uniformly distributed when the machining gap, rotation speed and eccentricity of the polishing plates were correctly selected. The best simulation results were found using the same parameter set as in the experimental results.The newly developed polishing method, clustered magnetorheological finishing (CMRF), was applied to polish


Attention-deficit/hyperactivity (ADHD) and autism spectrum (ASD) disorders often co-occur. In both cases, response inhibition deficits and inhibition-related atypical brain activation have been reported, although less consistently in ASD. Research exploring the overlap/distinctiveness between ADHD and ASD has significantly increased in recent years, but direct comparison of the inhibition-related neuronal correlates between these disorders are scarce in the literature. This study aimed at disentangling the shared and specific inhibitory brain dysfunctions in ASD and ADHD. Using functional magnetic resonance imaging (fMRI), brain activity was compared between children with ADHD, ASD and typically developing (TD) children aged 8–12 years during an inhibition stop-signal task, using stringent inclusion criteria. At the behavioural level, only children with ADHD exhibited inhibition deficits when compared with the TD group. Distinct patterns of brain activity were observed during successful inhibition. In children with ADHD, motor inhibition was associated with right inferior parietal activation, whereas right frontal regions were activated in children with ASD. Between-group comparisons disclosed higher middle frontal activation in the ASD group compared with the ADHD and the TD groups. Our results evidence different patterns of activation during inhibition in these two disorders, recruiting different regions of the fronto-parietal network associated to inhibition. Besides brain activity differences, behavioural inhibition deficits found only in children with ADHD further suggest that reactive inhibition is one of the core deficits in ADHD, but not in ASD. Our findings provide further evidence contributing to disentangle the shared and specific inhibitory dysfunctions in ASD and ADHD. Since the publication of the DSM-V, both diagnoses should be given when both ASD and ADHD diagnostic criteria are met1. ADHD is the most common comorbid psychiatric condition in referred populations of youths with ASD, with studies reporting comorbidity rates as high as 71%3. ASD traits are also common in children and adolescents with ADHD, with 12.4% having an ASD diagnosis4. Consequently, an increasing number of studies are investigating the overlap and distinctiveness between these disorders at the cognitive, clinical and biological level, to determine whether they are sufficiently distinct to be considered separate disorders, or rather represent the extremes of an underlying continuum5. Particularly, the Research Domain Criteria initiative calls for further investigation of transdiagnostic phenotypes and deficits, and their neural underpinnings6.Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have distinct core diagnostic criteria but often co-occur. ADHD is characterized by impairing levels of inattention and/or hyperactivity–impulsivity, and ASD is defined by deficits in social communication and interaction, and the presence of restricted, repetitive behaviours, interests or activities8. Response inhibition is one of the neurocognitive domains most affected in ADHD, in experimental paradigms such as the stop-signal task (SST)9, which measures the ability to cancel an ongoing motor response10. This paradigm is unique in allowing the estimation of the stop process covert latency: the stop-signal reaction time (SSRT), a sensitive individual index of inhibitory ability10. Nevertheless, previous studies using the SST in ASD populations have reported mixed findings, some showing longer SSRTs in ASD compared with typically developing (TD) children14 and others not19. Importantly, comorbid ADHD seems to partly explain inhibitory deficits in individuals with ASD14.At the neuropsychological level, there is evidence for inhibition deficits in both ADHD and ASD20. Nevertheless, a recent study evidenced higher activation in a group of non-comorbid medication-naive children with ADHD relative to TD children in regions comprising the fronto-basal ganglio-thalamo-cortical system associated with inhibition21.It is still unclear whether these deficits are driven by the same underlying brain mechanisms. A task-specific meta-analysis evidenced reduced activation in bilateral inferior frontal gyri, the right superior frontal gyrus and the right middle frontal gyrus (MFG) in children with ADHD compared with TD children during the SST17. Gooskens et al.17 found no differences in activation during successful stopping between children with ASD and TD participants. Instead, higher ADHD symptomatology was associated with increased inhibition-related activation in the left and right frontal and middle cingulate regions, linking changes in cognitive control in ASD to the presence of ADHD symptoms. In a pharmacological fMRI study, Chantiluke et al.16 compared inhibition-related brain activation during the SST between non-comorbid ASD, non-comorbid ADHD and TD samples of children and adolescents. In the placebo condition, participants with ASD showed increased activation compared with ADHD in the left middle and inferior frontal cortex (IFC), the left orbitofrontal cortex (OFC), the left superior temporal lobe and the basal ganglia. In addition, opposite disorder-specific brain activation patterns were revealed: there was reduced activation in the left OFC and basal ganglia in the ADHD group, but enhanced activation in the left and right IFC in children with ASD.In ASD samples, only two functional magnetic resonance imaging (fMRI) studies have investigated inhibition-related brain activity using the SSTIt is important to note that fMRI studies using the SST in populations with ADHD and/or ASD vary in their sample characteristics regarding age range, gender, medication history, clinical subtype and/or comorbidities, hindering the characterization of inhibition-related activation patterns in each disorder. In addition, studies differ in their SST design, the within-subject level contrasts, but also the exclusion criteria used based on task performance, hence potentially compromising the validity of the reported results.16, it is deemed too premature to draw conclusions regarding the shared and disorder-specific brain inhibition-related dysfunctions between these disorders. In the present fMRI study, brain activation during the execution of an SST was explored in a sample of children with ASD or ADHD and in a group of TD children. To capture inhibition ability as accurately as possible, SST performance exclusion criteria and analysis were based on recently provided consensus recommendations by Verbruggen et al.22 on the correct implementation of the task and data analysis.To sum up, a growing functional neuroimaging literature has evidenced brain inhibitory dysfunction in both ASD and ADHD. Nevertheless, with only one pharmacological fMRI study directly comparing ASD and ADHD samplesBased on previous literature, we hypothesized that (1) children with ADHD and ASD would show longer SSRTs compared with TD children, and that children with ADHD would further exhibit longer SSRTs compared with the ASD sample; (2) children with ASD would show higher activation in the left middle/IFC and right IFC, and children with ADHD would show underactivation in a fronto-basal ganglia network compared with TD children.23 criteria for ADHD and the parents of all participants responded to the ADHD Rating Scale-IV24, assessing the severity of ADHD symptoms. ASD was assessed according to the Autism Diagnostic Interview-Revised, the Autism Diagnostic Observation Schedule and/or the Childhood Autism Rating Scale27. Exclusion criteria for all participants were as follows: history of prematurity, neurological disorders, genetic disease, complications during labour with neonatal care unit hospitalization, disabling somatic pathology with a potential psychological impact and contraindications to MRI. All participants had a General Ability Index higher than 70 on the Wechsler Intelligence Scale for Children28 and one participant with ASD had a Leiter performance score of 7430. All participants with ADHD were medication-naive and did not present any psychiatric comorbidity. In the ASD group, nine participants also had an ADHD-like comorbidity. In addition, only one participant had a history of psychostimulant (methylphenidate) and antipsychotic (risperidone) intake, and another participant was taking antipsychotic medication at the time of the scanning (Abilify).Seventy-four right-handed children aged 8–12 years were enroled in this study. Twenty-nine participants were excluded: 1 participant after the fortuitous discovery of a brain anomaly, 2 participants because of feeling anxious or claustrophobic in the scanner, 18 participants due to inappropriate SST performance and 8 participants for excessive head motion (see below). The final sample consisted of 18 children with ADHD (combined-type), 13 children with ASD and 14 TD children  on go trials minus mean SSD, where n equals the number of RTs in the RT distribution on go trials multiplied by the overall p. Longer SSRTs indicate poorer response inhibition. Other measures of interest for task performance are reported in Table 31, participants meeting any of the following criteria were excluded: percentage of successful stop trials lower than 25% or higher than 75%, percentage of correct responses on go trials inferior to 60% and an RT on unsuccessful stop trials numerically longer than RT on go trials.The SST measures the ability to cancel a previously triggered motor response to a go signal, when it is unpredictably followed by a stop signal shortly after. The interval between the go and the stop signals or SSD  was initially 250 ms, then adapted by 50 ms steps using a horse-race model  aimed at an equal distribution of failed and successful inhibition trials . As part of the demographic parameters and SST behavioural outcome measures were not normally distributed or the assumption of homogeneity of variances was violated, and our sample was relatively small, non-parametric analyses were reported.3. Two initial dummy scans were acquired and then discarded for the analysis. Twenty-seven slices were acquired in an ascending and interleaved order, covering the whole brain. Anatomical images were obtained using a T1-weigthed sagittal three-dimensional TFE (turbo field echo) sequence: TR = 8.644 ms, TE = 3.244 ms, inversion time = 450 ms, flip angle = 12°, field of view = 240 × 240 mm2, matrix size = 256 × 256 × 128 and voxel size = 0.94 × 0.94 × 1.2 mm3. Stimuli were displayed on an MR-compatible backward projection screen visible to the participant through a mirror in the MR head coil. Stimuli were presented using MATLAB (The MathWorks). Motor responses were recorded using a standard MRI-compatible response device .Imaging data were collected using a Discovery MR750w 3.0T scanner  at the UZ Brussel hospital (Belgium). A T2*-weighted spin echo planar imaging sequence was used: repetition time (TR) = 3000 ms, echo time (TE) = 70 ms, flip angle = 90°, matrix size = 128 × 128 × 27 and voxel size = 1.88 × 1.88 × 5 mm32) implemented in MATLAB2019a (The MathWorks). Pre-processing steps consisted on slice timing correction, realignment, normalization and smoothing with a Gaussian kernel 8 mm full-width at half maximum. Scan-to-scan motion was assessed using ArtRepair33. Scans with more than 1.0 mm scan-to-scan movement or more than 1.5% deviation from the average global signal were replaced using a linear interpolation of the neighbouring scan values. Images with total movement > 3 mm were repaired. Participants with more than 30% corrected scans were excluded from further analyses.fMRI data were pre-processed and analysed using SPM12 . To isolate the withdrawal component of inhibition34, a contrast map depicting areas of greater activity on “Successful Stop versus Go” trials was created for each subject. Time and dispersion derivatives were included in the model. Twenty-four nuisance motion regressors were added to the GLMs to further control for movement-related effects in fMRI time series35. Events were modelled at the time of the go stimulus onset with a duration of 1.5 s36. Initial inspection of individual SPM maps revealed no signs of violation of design orthogonality; subsequent calculation indicated that the variance inflation factor was 8.44 for successful stop signal items and 12.48 for successful go items. Individual subject contrasts were entered into a second-level analysis to estimate between-group differences using a one-way analysis of variance (ANOVA). The initial voxel threshold was set to 0.001 uncorrected. For the whole-brain analysis, only clusters with pFWE-corrected < 0.05 are reported, accounting for multiple comparisons. In addition, a region-of-interest (ROI) analysis was performed in a priori determined locations corresponding to regions reported to exhibit abnormal inhibition-related activation in both ASD and ADHD compared with TD participants, or between-disorders differences, i.e., the bilateral IFC, left inferior parietal lobe, left superior temporal lobe, the right anterior cingulate cortex (ACC), the right posterior cingulate gyrus, the left MFG, bilateral insula, thalamus, caudate and precuneus21. ROI masks were created using the Anatomical Automatic Labelling toolbox37. Within-mask area inferences were computed using a statistical threshold of pSVC-FWE < 0.05 at the peak level. The p-value was Bonferroni-adjusted for the number of regions examined: pSVC-FWE = 0.05/10 = 0.005. Extraction of β-values and percentage signal change for illustrative purposes was made using RFXplot38. SSRT values , which is typical in this population9. Groups differed significantly on the ADHD-RS-IV scores . The ADHD group total score was significantly higher compared with that of ASD (p = 0.018) and TD children (p < 0.001) scores. The total score was also significantly higher for the ASD group compared with that of the TD group (p = 0.045), due to the presence of ADHD comorbidity in nine participants (Supplementary Table p < 0.001) and the ASD groups (p = 0.016). The hyperactivity score was significantly higher in the ADHD group compared with that of the TD group (p < 0.001). Finally, there were no significant differences between groups regarding head motion. Additional analyses including non-comorbid ASD and ASD with ADHD comorbidity separately are reported in the Gender was not significantly related to diagnosis and groups did not differ on age Table . IQ was p = 0.029), MRTs (p = 0.003) and mean RT on unsuccessful stop trials (p = 0.004), as compared with the TD group. No other significant differences were found for task performance  in a cluster comprising the right angular gyrus, the right inferior parietal cortex and the intraparietal sulcus in the ADHD group (Table pFWE < 0.05). For these whole-brain analyses, results were comparable with or without age entered as a covariate. The TD group did not show activation at pFWE < 0.05 .The whole-brain analysis of the Successful Stop versus Go contrast disclosed increased activity (up Table . In the pSVC-FEW < 0.005). No ROI survived Bonferroni correction within the ASD and the TD group.The ROI analysis revealed a significant effect after Bonferroni correction in the right posterior cingulate cortex in the ADHD group  and peak-level pFWE < 0.05 . Pairwise comparisons showed that the group effect in this region was due to a significant higher activation in the ASD group during successful inhibition compared with the ADHD group  and peak level  during successful inhibition  and ASD with ADHD comorbidity (n = 9). A small volume correction with a 10 mm sphere radius centred on the three coordinates reported above was examined at the peak-level pSVC-FWE < 0.05. Activations remained significant in all these regions, suggesting that between-group differences were not explained by the presence of comorbid ADHD in nine patients with ASD.is Table  revealedion Fig. . ResultsFinally, conjunction analysis did not show common activations between the patients groups.ROI analyses: Analyses conducted on ROIs disclosed higher activation in the ADHD than the TD group in the right posterior cingulate gyrus  after Bonferroni correction. No other ROIs survived Bonferroni correction.9. No differences in task performance were found between children with ASD and TD children, consistent with Schmitt et al.19, who found that the ability to reactively stop behaviours as measured by the SSRT was preserved in a group of children and adults with ASD. Nevertheless, in their study, RT slowing during go trials was reduced in patients compared with controls, suggesting a deficit in strategically delaying the onset of behavioural responses, i.e., proactive control.In this study, both inhibition deficits and slower responding were found in the ADHD as compared with the TD group at the behavioural level, consistent with previous studies41. These regions have been associated with action cancellation and action withholding42.fMRI analyses revealed different activation patterns in children with ASD and ADHD when explored separately, even if nine participants in the ASD group had an ADHD-like comorbidity. Conjunction analysis did not show common activations between the patients groups, emphasizing the between-group distinctiveness in activation patterns. In the ADHD group, successful inhibition activated a cluster comprising the right angular gyrus, the intraparietal sulcus and the inferior parietal gyrus, in line with previous studies in children and adolescents with ADHD42. These results show that response inhibition may place different demands on the fronto-parietal network in ASD and ADHD, eventually activating regions that serve different functions in response inhibition. Although the inferior parietal cortex has been associated with rapid movement prevention43 and attentional capture of infrequent stimuli44, its contribution to inhibition per se remains unclear. The recruitment of parietal nodes in children with ADHD suggests they may need a higher involvement of attentional processes for successful inhibition than children with ASD, thereby modulating the response inhibition process.In children with ASD, activation was found mostly in frontal regions, in particular, the right MFG, also associated with action withholding and cancellation16, who also found increased activation in the left middle/IFC in boys with ASD as compared with those with ADHD, for a comparable task performance. In addition, there was also higher activation in the right MFG in the ASD group as compared with the TD group during successful inhibition. These results suggest that children with ASD may require recruiting more of these brain areas to achieve a task performance comparable to the ADHD and the TD groups. Differences observed in the left frontal cortex indicate that the left hemisphere should not be neglected in the study of response inhibition in ASD, as previously suggested45.Between-groups comparisons disclosed higher activation in the ASD than the ADHD group in the right cingulate gyrus/MFG and the left MFG. These results are consistent with Chantiluke et al.47. However, this difference did not survive Bonferroni correction and the result should thus be considered with caution. The ACC has been associated with conflict and performance monitoring and salience detection during response inhibition49. Finally, ROI analysis revealed inhibition-related differences between the ADHD and TD groups in posterior cingulate activity, which is consistent with recent preliminary meta-analysis results50.Regarding the ACC, ROI analysis revealed higher inhibition-related activity in the ASD than in the TDC and the ADHD groups, in agreement with previous studies evidencing atypical brain activity during response inhibition in autism22 to analyse SST data in such a way that the ability to inhibit is accurately captured. Such precautions have often been dismissed or have been applied in a more lenient way in previous studies. Therefore, we argue that the strict exclusion criteria used actually represents the strength of our study, admittedly at the cost of a reduced sample size. A related limitation is that our sample was only composed of individuals able to perform the task inside of the scanner and whose data were included after data quality control regarding task performance and head motion. Consequently, the results of our study may not be generalizable to all individuals with ASD and/or ADHD, and especially those who display more severe ASD and/or ADHD symptoms or exhibit lower cognitive abilities. Our gender balanced sample was however large enough to detect robust significant brain activation differences between groups in our whole brain analysis, even after correction for multiple comparisons. In addition, all children with ADHD were non-comorbid and medication naïve in order to avoid drug-induced brain changes previously evidenced in other studies16 and only one participant with ASD had a previous history of psychostimulant intake. Therefore, there was no potential confounding effect of medication and/or comorbidity on inhibition-related activity in the ADHD sample. Again however, strict inclusion criteria may hinder the generalization of our results to the heterogeneous population of ADHD patients who present various comorbidities and/or medication status in clinical practice. Finally, the power of our statistical design was somehow limited by the use of short inter-stimuli intervals including jitters (leading to increased variance inflation factors) aimed at keeping the task duration as short and motivating as possible for our specific populations, whose inclusion in fMRI studies remain a challenge.The findings in this study should be considered in the light of some limitations. First, our final sample was relatively small due to strict exclusion criteria regarding head motion and/or non-adequate SST performance. The strict but necessary exclusion task performance criteria were based on the latest consensus recommendations by Verbruggen et al.3, but potentially confounding our findings. Nevertheless, similar results were found regardless of ADHD comorbidity. The distinct patterns of activation between disorders suggest that ADHD-like symptoms in ASD are associated with a different pattern of activation compared with non-comorbid ADHD and raise the question of the distinctiveness between ADHD-like symptoms present in ASD and pure ADHD, which constitutes a crucial clinical insight in the study of the comorbidity between ASD and ADHD.Finally, there was a high percentage of children in the ASD group, who had an ADHD comorbidity, which is consistent with recent comorbidity reportsTo conclude, a distinct pattern of activation during successful inhibition in a sample of children with ADHD and a sample of children with ASD was evidenced for the first time using the SST, despite the presence of ADHD-like symptoms in nine participants in the ASD group. Children with ADHD showed activation in inferior parietal regions and children with ASD in bilateral middle frontal regions during successful inhibition as compared with go trials. The ASD group showed higher activation in the middle frontal cortex compared with TD participants, whereas the ADHD showed higher activation than the TD group in the right posterior cingulate. Importantly, only the ADHD and not the ASD group showed behavioural inhibition deficits compared with the TD group, reinforcing the idea that reactive inhibition is one of the core deficits in ADHD, but not in ASD. Future work should explore the commonalities and distinctions in inhibition-related activity in a larger sample of children with ASD fractionating the sample in terms of the presence or absence of ADHD-like symptoms and a sample of children with ADHD, to examine the differences between the ADHD-like comorbidity in ASD and a primary diagnosis of ADHD.Supplementary Material


Bartonella spp are abundant in skin lesions resembling striae distensae. These striae distensae‐like lesions, coincidental with sudden onset of neuropsychiatric symptoms, indicate testing for suspected Bartonella spp. infection.We demonstrate  Bartonella spp are abundant in skin lesions resembling striae distensae. These striae distensae‐like lesions, coincidental with sudden onset of neuropsychiatric symptoms, indicate testing for suspected Bartonella spp. infection.We demonstrate  They are re‐emergent, often‐neglected, stealth bacteria, with worldwide significance for zoonotic infections. Bartonella spp. are aerobic, gram‐negative, vector‐borne bacteria that can infect many different cell types including endothelial cells and erythrocytes of numerous mammalian hosts, causing prolonged infections in humans and animals with diverse disease manifestations.Bartonella quintana has been detected in 4000‐year‐old human remains, representing the oldest evidence of this infection to human beings.Bartonella spp. are transmitted to humans by various blood‐sucking arthropod vectors and by animal bites and scratches.Bartonella spp. to animals and humans. Bartonella henselae can multiply in the cat flea and persist in flea feces in the environment for at least 9 days.Bartonella spp. infection may establish a primary dermal niche that likely includes the vascular endothelium. Inside the mammalian reservoir host, the infection spreads to the blood, a secondary niche, where bacteria invade erythrocytes and a long‐lasting bacteremia develops, a hallmark of Bartonella infection.Bartonella spp. is potentially fatal, especially in immunodeficient patients, while immunocompetent individuals are at risk of chronic infection.After an intradermal inoculation by the vector, the Bartonella spp. infection. False‐negative results are frequent, even with multi‐step molecular and microbiological techniques and serology.Bartonella spp. infections remains challenging, warranting development of sensitive and reproducible diagnostic methods.There are no gold‐standard diagnostic tests to confirm Bartonella‐immunoreactivity in skin tissue biopsies of three subjects with psychiatric symptoms, using single‐photon confocal laser scanning microscopy; and the alteration in dermal collagen fiber organization in the nonclassical striae‐like skin track lesions using multi‐photon (SHG) laser scanning microscopy on multi‐stained thick tissue skin samples. Patients' blood and skin were also analyzed by PCR, hemi‐nested PCR, or enrichment culture.In this study, we report the detection of 22.1B henselae, Borrelia burgdorferi, and Babesia microti. At age 14, during antibiotic treatment for his tick‐borne infections, asymmetric striae‐like skin tracks appeared on his arms and back. B henselae and B quintana IFA titers were 1:256 against both strains, indicative of Bartonella exposure. Enrichment blood culture/PCR testing for Bartonella bacteria were negative in this patient.B henselae DNA was amplified and sequenced from the skin tissue biopsy. Skin tissue biopsies were collected from a skin track lesion and from nonlesional skin and immunostained with anti‐Bartonella spp..Bartonella spp.in both tissues (data not shown).Seven‐year‐old male presented at a psychiatric clinic with mood swings, decreased need for sleep, oppositional behavior, and refusal to attend school. Subsequent diagnoses included bipolar disorder‐type II, obsessive‐compulsive disorder, separation anxiety disorder, sensory hypersensitivity, and seasonal affective disorder. He was treated with Omega‐3, mood stabilizers (Depakote), antianxiety medication (Buspar), and light therapy. At age 11, he developed severe headaches and stomach bloating. His school attendance remained erratic because of excessive fatigue. Evaluation for underlying infectious triggers revealed evidence of exposure to 2.2Bartonella as the causal agent, obtained Bartonella serology, and biopsies of a skin track lesion and nonlesional skin. Laser scanning microscopy revealed more immunoreactive‐B henselae in the lesional biopsy vs in the skin from the nonlesional biopsy. Second harmonic generation (SHG) microscopy revealed that the dermal collagen fibers of the lesional skin were scar‐like, disordered, and nonaligned  infusions, several Transcranial Magnetic Stimulation sessions plus a variety of psychotherapeutic interventions. An integrative physician, who noted multiple striae‐like skin tracks on his torso and therefore suspected 2.3Bartonella spp. infection was considered. Blood samples were collected at different time points over the next 4 years. Bartonella henselae (San Antonio 2 strain type) and B vinsonii subsp berkhoffii (genotype I) DNA were PCR amplified and sequenced from his blood or enrichment blood cultures at multiple time points prior to and during the antimicrobial treatment period. Each blood specimen was processed using a multi‐PCR platform protocol with an insect cell culture‐based liquid growth medium for bacterial enrichment. Prior to beginning antibiotic therapy, a skin biopsy from a nonlesional area of the patient's calf was acquired, multi‐stained with Bartonella spp.‐specific antibody, and imaged using laser scanning confocal microscopy. Bartonella henselae immunoreactivity was visualized  imaging to determine the directionality and organization of the skin fibrillar collagen in the biopsies of both lesional and nonlesional skin. The 3D images created by SHG technology allow for delineation of collagen fiber arrangement in the dermis, while facilitating discrimination between normal and scarred skin tissue. SHG microscopy is ideal for the analysis and quantification of the spatial arrangement of collagen fibers in tissue and has emerged as a useful tool for studying key features of collagen remodeling.Bartonella spp. infection support evolving evidence that striae‐like skin track lesions can develop in different body areas during the course of their illness. Bartonella‐associated skin lesions are usually described as unilateral linear inflamed skin tracks that change in color and appearance with disease progression and in association with antimicrobial treatment. These skin lesions occur without preceding or accompanying predisposing factors, such as obesity, weight loss, growth spurts, excessive body tension, or corticosteroid intake. These clinical features of Bartonella‐associated skin tracks make them distinct from the traditional striae distensae, commonly known as stretch marks, which arise due to stretching of the dermis. Clinically, classical striae distensae involves the abdomen, breasts, buttocks and thighs, lumbosacral area and upper arms,Clinical observations from physicians treating patients with chronic Bartonella spp. in patients with neuropsychiatric symptoms.B henselae bacteremia may contribute to progressive, recalcitrant neuropsychiatric symptoms consistent with Pediatric Acute‐onset Neuropsychiatric Syndrome (PANS) in a subset of patients and may be misdiagnosed as schizophrenia.Bartonella spp. infection may play a role in some cases of pediatric mood disorders.Bartonella spp. associated striae‐like skin tracks alerted clinicians to the possibility of B henselae in the differential diagnosis of these somewhat treatment resistant neuropsychiatric cases. This association requires more rigorous testing as it may be that mental health clinicians should consider asking about the presence of Bartonella‐associated skin tracks during their assessment of new patients or those that undergo changes in symptomatology over time.There is increasing microbiologic evidence indicating bloodstream and cerebrospinal fluid infections with one or more Bartonella spp. infection in all patients. As an example, a recent study found that almost 50% of patients with nonspecific symptoms had positive Bartonella spp. serology and/or were Bartonella PCR/DNA sequence positive for one or more Bartonella spp. In that study, Bartonella spp. bacteremia was confirmed in one in four patients.Bartonella bacterial infection cannot be confirmed by PCR, diagnosis often relies on a combination of clinical, epidemiological, and serologic criteria.Bartonella antigens, serology can lack sensitivity, can only implicate prior Bartonella spp. exposure, may be associated with cross reactivity with members of other generaBartonella spp. infection. Laser scanning microscopy imaging of multi‐stained tissue biopsies has proven to be consistent, providing reliable detection of Bartonella spp and Borrelia burgdorferi. (data not shown). This technique has been primarily a research tool but we envision this technique in a diagnostic modality. This tool is currently commercially available for quantifying epidermal nerve fibers for diagnosing peripheral neuropathy. The work described here indicates the potential use of this imaging technique with a 2‐week turn‐around time. Indeed, it seems we need a toolbox of testing modalities to detect, diagnose and understand disease pathology in suspected Bartonella spp. infection.It is also evident that currently no single diagnostic methodology will confirm 4B henselae infection in patients with striae distensae‐like lesions coincidental with sudden onset of neuropsychiatric symptoms. We used advanced imaging methods to characterize immunoreactive (ir)‐B henselae in skin biopsies, finding more ir‐B henselae in lesional vs nonlesional skin. Additionally, the imaging studies demonstrate dermal ir‐B henselae bacteria is found both within and outside of blood vessels. Second harmonic generation imaging (SHG) of dermal collagen fibrils, reveals that the dermal collagen in lesional tissue is disrupted and scar‐like. Advanced imaging of multi‐stained thick skin biopsies can help elucidate pathogenesis and may provide an important tool for diagnosis. Though this case report focused on describing the striae‐like lesions observed in patients infected with Bartonella spp. infections, we recognize the importance of coinfections as we and others expand our future studies to include other coinfections and diseases such as Morgellons disease.Bartonella spp. infection.In this case report, we used multiple techniques to detect Breitschwerdt EB in conjunction with Sushama Sontakke and North Carolina StateUniversity holds US Patent No. 7,115,385, Media and Methods for Cultivation of Microorganisms, which wasissued October 3, 2006. Breitschwerdt EB is a founder, shareholder and the chief scientific officer for GalaxyDiagnostics, a company that provides diagnostic testing for the detection of Bartonella spp. infection inanimals and human patients. The remaining authors have disclosed no conflicts of interest.AM, ME, and EB: designed the study. RG, EB, and RM: provided regents and clinical expertise. AM, JD, and ME: performed all microscopy related experiments. EB and LB: provided molecular analysis. All authors contributed equally to the writing process and accepted the final version.


An uncross‐lateralised typically developing (TD) group (n = 32) was also identified, and included as a control. Motor performance was assessed with seven valid and reliable fine and gross motor tasks performed with both preferred and non‐preferred limbs. Group, task and sex‐related effects were examined. Findings revealed that male  cross‐lateralised children with ADHD performed significantly worse, respectively, in two of the fine motor tasks . Results suggest that cross‐lateralised hand and foot preference may affect complex motor skills in male children with ADHD. Furthermore, characteristics of ADHD may manifest differently in male and female children. Findings highlight the importance of considering both hand and foot preference when targeting motor interventions for children with ADHD.Cross‐lateralisation and increased motor difficulties have been reported in children with attention‐deficit/hyperactivity disorder (ADHD). Nevertheless, the question of how crossed (i.e. mixed preference) or uncrossed (i.e. same side preference) lateralisation impacts motor performance in children with ADHD has yet to be examined. In this study, previously validated observational measures of hand and foot preference were used to identify right‐handed children with ADHD who display cross‐ ( Attention‐deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurobehavioural disorder . Seldom displayed in typical development, the occurrence of cross hand‐eye lateralisation is more prevalent in children with developmental learning and reading challenges, and ambiguous lower limb preference has been reported . Children with ADHD were recruited from elementary schools for children with specific learning disorders identified by the National Institute for Pedagogy and Psychology. Children were diagnosed at 5–6 years. As per Czech legislation, assessment is repeated annually using standard tools: the Statistical Classification of Diseases and Related Health Problems  from the Czech Republic ; (2) uncross‐lateralised children with ADHD  and (3) uncross‐lateralised typically developing control group, .To ensure consistency in study procedures, one researcher completed all data collection and scored all measures. To identify whether children displayed crossed or uncrossed‐lateralisation, previously validated observational measures of hand and foot preference were used Musálek, . ChildreAs this study intended to extend the work of Scharoun et al. , the samA paper and pencil task, participants traced between lines of a spiral as quickly and accurately as possible. The largest diameter of the spiral was 41 mm, and the width was 2 mm. Participants were not permitted to reposition the sheet. Drawing outside the lines and/or touching the lines was deemed an error and penalised by 2 seconds. Time to completion (i.e. the moment the participant's pen crossed the finish line) was recorded for separate trials with preferred and non‐preferred hands.Also a paper and pencil task, participants were presented with a sheet of 90 identical circles (2 mm diameter) and asked to place a dot in as many circles as possible within 30 seconds. Separate preferred and non‐preferred hand trials were performed. Only dots placed within the circles were counted.This task included two open matchboxes, placed one behind the other , and a pair of tweezers placed on the desk 150 mm in front of the participant. The matchbox closer to the participant was filled with 20 beads (5 mm in diameter), and the second was empty. Participants were instructed to use the tweezers to move as many beads as possible (one by one) from the full box to the empty box. The number of beads transported in 30 seconds was recorded for separate preferred and non‐preferred hand trials.This task included a closed matchbox placed on the table in front of the participant at the midline. Starting in a pronated position and holding the matchbox between the thumb and index finger, the task involved using one hand to rotate the box 180°. The box had to touch the table at all times; therefore, it could not be lifted. Each trial (preferred and non‐preferred) took 30 seconds. Performance was video recorded and coded offline using Dart fish . Each correct 180° rotation (i.e. twist) was recorded.The Lafayette Tapping Board Test (Lafayette Instruments Co.) was used. The electronic plate (30 cm in length) with two metal surfaces was placed at the participant's midline. The electronic plate was connected to an electronic meter through a cable. A metal tool, connected to the plate with a cable, was used for tapping with one hand. The participant was asked to tap the metal surfaces of the plate with the metal tool for 30 seconds . While the number of taps was recorded by the instrument on a digital display, the display was not shown to participants, to prevent any undue influence on performance.The task included two square targets  attached to a Table 70 cm apart from each other and a middle target (15 cm side) with a picture of a hand in the middle. Starting with the designated hand on the middle target, participants were required to tap the targets, when signalled, alternating between left and right. Each trial took 30 seconds . Performance was video recorded and coded offline using Dart fish . The number of taps was recorded.This task required the participant to stand next to a table with his/her preferred leg adjacent to the table. Subsequently, the participant tapped his/her foot in front of and behind the standing leg, alternating between heel and toe for 30 seconds . Performance was video recorded and coded offline using Dart fish . The number of taps was recorded.p < .05. Only results with large effect sizes (Hays (ω2 > .14) were considered , circularity and covariance matrices equal (Box‐M test) were verified. Data were then entered into a three‐way repeated measures analysis of variance (ANOVA). The within‐subjects factor was the limb used to complete the task . Between‐subjects factors included group  and sex . Subsequently, data were analysed using separate one‐way analyses of variance (ANOVAs) that considered the hand used to complete the task and sex. Statistical significance was set at 2, we used repeated measures ANOVA as well as separate one‐way ANOVAs.Assumptions for ANOVA were verified in exploratory data analysis (EDA). In four of the seven tests  data normality was confirmed. In Spiral tracing, Twisting box and Large plate tapping, normality was rejected in at least one test Table ; howeverOverall, children performed significantly better with the preferred hand/foot compared to the non‐preferred hand/foot. With the exception of twist box, children with ADHD performed all tasks significantly worse than TD children. Moreover, spiral tracing, twist box and small plate tapping revealed significant differences between females and males. Results from each task are described in detail below.F = 15.19; MSE = 902.29, p < .01, Hays ω2 = .06. Furthermore, females (x‾=42.9±10.43) were faster than males (x‾=46.6±13.7)F = 10.92, MSE = 648.64, p < .01, Hays ω2 = .07. Finally, performance with the preferred hand (x‾=35.23±5.54) was faster than non‐preferred (x‾=54.46±9.55) F = 504.48, MSE = 16,758, p < .001, Hays ω2 = .28.Significant main effects of group, sex and limb were revealed. Children with ADHD performed significantly worse than their TD peers, x‾=41±4.42) with the preferred hand than uncross‐lateralised males with ADHD (x‾=36.7±5.32) and typically developing males (x‾=31.5±3.77),F = 16.00, MSE = 340.21, p < .01, Hays ω2 = .39. On the other hand, cross‐lateralised females with ADHD did not perform differently than uncross‐lateralised females with ADHD  = 15.79, MSE = 169.51, p < .01, Hays ω2 = .08, compared to TD peers. Males and females did not differ (p > .05). All participants made significantly more dots with the preferred hand F = 465.86, MSE = 6528.92, p < .001, Hays ω2 = .23, (x‾=28.5±5.48) compared to the non‐preferred hand (x‾=16.5±3.31). A significant interaction was found between group and limb, F  = 15.86, MSE = 169.51, p < .01, Hays ω2 = .09; Figure Children with ADHD made significantly fewer dots, F, 184 = 1x‾=23.07±4.31) compared to uncross‐lateralised males with ADHD (x‾=27.66±4.77) and TD males  = 22.87, MSE = 483.72, p < .01, Hays ω2 = .48). Further, no significant difference was revealed between females with and without ADHD in non‐preferred hand performance (p > .05).Results from separate ANOVAs that included sex as a factor revealed that cross‐lateralised males with ADHD made significantly fewer dots with the preferred hand (p > .05). Nevertheless, the main effect of limb revealed all participants moved significantly more beads with the preferred hand, F = 153.17, MSE = 376.14, p < .001, Hays ω2 = .42; x‾=14.3±2.54, compared to the non‐preferred hand (x‾=11.3±2.3). A significant interaction between limb and sex, F = 79.95, MSE = 196.33, p < .001, Hays ω2 = .24, revealed a significant difference in preferred and non‐preferred hand performance exclusive to male participants. The interaction between group and limb revealed children with ADHD moved significantly fewer beads compared to their TD counterparts, F = 22.82, MSE = 107.88, p < .01, Hays ω2 = .14, when using the preferred hand. The three way interaction between group, sex and limb, F = 9.50, MSE = 36.61, p < .01, Hays ω2 = .31; Figure x‾=13.5±2.1) moved fewer beads with the preferred hand compared to TD males (x‾=16.6±1.54).Main effects of group and sex were not significant  = 3.61; MSE = 8.85, p = .034, Hays ω2 = .05. However, subsequent power analysis revealed a low level of task power (65%), and thus, significance of the observed difference between average performances of the participants, was insufficient.Results from separate ANOVAs revealed that cross‐lateralised children with ADHD displayed a significantly smaller difference in performance between the preferred and non‐preferred hands, x‾=34±6.1) than males  = 18.41, MSE = 508.71, p < .01, Hays ω2 = .15). Moreover, a significant effect of limb, F = 39.39, MSE = 575.50, p < .001, Hays ω2 = .21, revealed the box was twisted significantly more times with the preferred hand (x‾=39.7±6.3) than the non‐preferred hand (x‾=36.1±4.9).No significant differences between children with ADHD and their TD peers emerged. It was observed that females scored significantly worse (p > .05). Females scored significantly better with the preferred hand compared to males, F = 13.16, MSE = 1308, p < .01, Hays ω2 = .14. A significant difference, F = 59.17, MSE = 6731.83, p < .001, Hays ω2 = .20, was observed in the number of taps completed with the preferred (x‾=119.2±12.5) and non‐preferred hand (x‾=107±10.9). Further, children with ADHD scored significantly worse than TD peers with both the preferred, F = 8.08, MSE = 1048, p < .01, and non‐preferred hand, F = 14.8, MSE = 1336.46, p < .01; Figure The effect of group was non‐significant; overall performance of children with ADHD and TD peers did not differ in small plate tapping  = 8.42, MSE = 284.29, p < .01, revealed a significant difference in preferred (x‾=64.2±6.3) and non‐preferred hand (x‾=61.8±5.4) performance; however, this difference had a low effect size (Hays ω2 = .04).No significant differences were found as a function of group or sex. A main effect of limb, p > .05). For all participants, the preferred foot (x‾=50±9.07) performed better than the non‐preferred one (x‾=46.5±8.2);F = 15.23, MSE = 533.77, p < .01, Hays ω2 = .15. Uncross‐lateralised children with ADHD performed significantly worse with the preferred, F = 4.91, MSE = 412.58, p < .01, Hays ω2 = .07, and non‐preferred foot F = 4.38, MSE = 45.26, p < .05, Hays ω2 = .08, than typically developing children. When performing with the preferred foot, females with ADHD  = 3.55, MSE = 245.98, p < .05; Hays ω2 = .11). Likewise, greater performance differences were found in non‐preferred foot performance when comparing TD females (x‾=51.7±9.5) to cross‐ (x‾=43.8±7.1) and uncross‐ (x‾=42.5±7.2) lateralised females with ADHD, F = 6.30, MSE = 370.88, p < .01, Hays ω2 = .19. No significant differences were observed in males  cross‐lateralised children with ADHD; (2) uncross‐lateralised children with ADHDand (3) an uncross‐lateralised TD control group. Based on findings from Scharoun et al. , we did We did, however, expect children with ADHD, and, cross‐lateralised children with ADHD specifically, would display less proficient motor skills. Findings offer partial support to our hypotheses. Children with ADHD displayed worse performance than TD peers on all tasks, except twist box, and cross‐lateralisation also had a significant impact on performance. Findings were more robust in male cross‐lateralised children with ADHD in two fine motor tasks , providing support for the notion that ADHD may manifest differently in male and female children. Findings will be discussed in the context of task‐ and sex‐related effects, with the discussion of differences between the limbs embedded within each section, where appropriate.In line with previous research , ADHD predominantly hyperactive–impulsive (ADHD‐H) and ADHD combined type (ADHD‐C). A systematic review from Kaiser et al.  revealed


ADHD = 42; Ncontrols = 50; NsubthreholdADHD = 15; range age: 13–29 years). The relative quantification of bacterial taxa was done using 16S ribosomal RNA gene amplicon sequencing. Beta-diversity revealed significant differences in bacterial composition between participants with ADHD and healthy controls, which was also significant for inattention, but showing a trend in case of hyperactivity/impulsivity only. Ten genera showed nominal differences (p < 0.05) between both groups, of which seven genera were tested for their association with ADHD symptom scores . Our results show that variation of a genus from the Ruminococcaceae family (Ruminococcaceae_UCG_004) is associated (after multiple testing correction) with inattention symptoms and support the potential role of gut microbiota in ADHD pathophysiology.Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Given the growing evidence of gut microbiota being involved in psychiatric  disorders, we aimed to identify differences in gut microbiota composition between participants with ADHD and controls and to investigate the role of the microbiota in inattention and hyperactivity/impulsivity. Fecal samples were collected from 107 participants (N Attention-deficit/hyperactivity disorder (ADHD) is a common, highly heritable , heterogTreatment of ADHD usually involves prescription of either stimulant or non-stimulant medication that target specific systems related to dopamine, noradrenaline, and/or serotonin neurotransmission . These pOne of the non-pharmacological interventions recently suggested to influence ADHD symptom severity is diet  ,13. DietBifidobacterium) associated with a neural hallmark of ADHD, for instance, decreased functional responses of the ventral striatum during reward anticipation [A key player in the gut-brain axis is the complex ecosystem of commensal bacteria living in our gut, the microbiota . DNA seqcipation .In the present study, we increased the sample size of Aarts et al. (16) (samples overlap ~40%) in order to investigate (i) if there are differences in gut microbiota composition between participants with ADHD and age matched controls, and if so, (ii) whether these bacteria are associated with the severity of ADHD symptoms.N = 42), subthreshold ADHD , and healthy controls (N = 50). The cohort included sibling pairs, which was taken into account in the analysis. A semi-structured diagnostic interview of DSM-IV criteria was conducted with both the participant and his/her parents using the Kiddie-Schedule for Affective Disorders and Schizophrenia (K-SADS) according to DSM-IV criteria. Clinical diagnosis was confirmed using a diagnostic algorithm which combined the diagnostic interview (K-SADS) with the Conners rating scales [To evaluate gut microbiota composition in participants with ADHD and healthy controls, we collected fecal samples from 107 Dutch (Caucasian) individuals enrolled in the follow-up of the NeuroIMAGE study . Three gg scales .Continuous measures of inattention severity (IA) and hyperactivity/impulsivity severity (HI) were derived from the Conners Adult ADHD Rating Scales  and Conners Teacher Rating Scale . For participants using medication, ratings were based on each participant’s functioning off medication. Detailed recruitment and diagnostic information can be found in the NeuroIMAGE design article .Additionally, the following information was obtained from medical records: age, sex, Body Mass Index (BMI), time delay between feces collection and symptom assessment , and use of ADHD-related medication. Five samples with missing BMI values were excluded from the regression analysis. For an overview of the participant characteristics, see Ethical approval for the study was obtained from the local research ethics committees (Commissie Mensgebonden Onderzoek (CMO) Regio Arnhem-Nijmegen) on 9 April 2013 and can be found under registration number 2012/542 and ethics protocol number NL nr.: 41950.091.12. A written informed consent was obtained from all participants and/or their parents prior to the sample and data collections.The human fecal samples were collected at home by the participants and stored at 4 °C. Within 24 hours after collection, the samples were transported to the laboratory, aliquoted into 1.5 ml Eppendorf tubes, and stored at -80 °C. The bacterial DNA was extracted using a repeated bead-beating step and the Maxwell® 16 Instrument , as described previously . DNA purThe sequenced data was analyzed through NG-Tax 16S rRNA pipeline at Wageningen University and Research  . NG-Tax We performed two filtering steps on the output file (BIOM-file) of NG-Tax in order to remove genera with low prevalence and to reduce the impact of the high number of absent genera . This step was critical to improve the power to detect the true effects of the microbiota while keeping as much information as possible. The way the genera/OTUs were identified made it impossible to disentangle if the observed values of zero correspond to true zeros  or are false zeros . The two filtering steps were applied as follows: (i) the OTU table was filtered at the genus level, where a genus with non-zero values in less than 10% of the samples was removed, and (ii) the OTU table was filtered at sample level, at which a sample with less than 10% of genera was removed . The resThe sequencing depth of the microbiota data was compared between all groups  by performing a Kruskal–Wallis H test on the total reads. This was done in order to assess equal distribution of the sequence reads across the groups, which helped to verify the effect of any technical variation between the groups. The results can be found in the Three alpha-diversity metrics were applied on the OTU level: (1) the species richness estimator, counting the observed unique OTUs in each sample ; (2) ShaTo assesses beta-diversity, we used the weighted UniFrac distance metric, a phylogenetic-based assessment of the difference in overall bacterial community composition at the OTU level . To analhttps://huttenhower.sph.harvard.edu/galaxy) for statistical analysis and visualization of the results. LEfSe uses non-parametric statistics  were selected for downstream correlation and linear regression analyses.Taxonomic composition of the gut microbiota was investigated at the phylum and genus levels after transforming the sequencing read counts into microbial relative abundance . Any unknown taxonomic level  was assigned to the next highest known taxonomic rank . The composition analysis was calculated using QIIME 1.9.1 with the “summarize_taxa.py” script . Microbie values ), in oure values , to idenLinear mixed regression analysis was performed to associate bacterial relative abundance with inattention or hyperactivity/impulsivity score available for all participants (including participants with “subthreshold ADHD”). Models were adjusted for age, sex, BMI, diff_days, and included the family relatedness as random factor; this was done by using the R function lme4::lmer. Given the skewed distribution of the microbial relative abundance prior to the association analyses, we investigated linear regression assumptions and identified and removed extreme and influential samples (outliers). Outliers are known to have a significant effect on the regression model (but not on the non-parametric test ). OutlieN = 19) were removed from the regression model to see how this effects the results.Often ADHD patients are medicated, and some studies show that medication can influence gut microbiota composition ,37. TherThe gut microbiota is a highly complex ecosystem of interacting organisms. In order to investigate the (in)dependent effect of the selected genera on symptoms, we investigated their correlation structure and performed multiple regression analysis. The genus-genus correlation was assessed based on Spearman’s rank correlation coefficient. Multiple regression analysis was performed for the same selected genera tested in the univariate models, adjusting for age, sex, BMI, diff_days, and family relatedness as a random factor; the analysis was done without the samples identified as outliers (see above). If not mentioned otherwise, the data were analyzed using IBM SPSS for Windows 25.0 software , and the analysis was preceded by the Shapiro–Wilk’s normality test.The general characteristics of the studied sample are presented in Within-and between-sample diversity metrics: None of the three alpha-diversity (within-sample diversity) measures showed significant differences between the ADHD and control groups , and inattention score (IA) , whereas hyperactivity/impulsivity score (HI) was at trend level . Age, sex, BMI, and medication did not have a significant effect on beta-diversity (Beta-diversity (between-sample diversity), assessed using betadisper , showed iversity . Additioiversity .Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Verrucomicrobia, were the most frequent phyla in our data . We did not find any association between tested genera and hyperactivity/impulsivity score ; therefore, only IA was taken into consideration in further analyses.At the genus level, differences in the gut microbiota composition revealed nominal significant case-control differences for ten genera (N = 19) from the analysis. We found that medication did not influence the association between Ruminococcaceae_UCG_004 and symptoms of inattention  versus non-medicated (N = 22) individuals with ADHD, we found that the genus Dialister was increased and that the genus Phascolarctobacterium decreased in medicated ADHD  or of false zeros .We tested the effect of ADHD medication on the (regression) results by excluding medicated cases  with each other. The other genera showed moderate (0.30 > r > 0.50), weak (0.30 > r > 0.10) or no correlation (Ruminococcaceae_NK4A214_group or Ruminococcaceae_UCG_005) did not significantly change the results. After controlling for other bacterial taxa , Ruminococcaceae_UCG_004 again showed the highest association with inattention score , followed by Ruminococcus_2 . These results suggest an independent effect of these genera on inattention  accounting for the effects of medication.Ruminococcaceae_UCG_004 genus was associated with ADHD inattention symptoms. Multiple regression analysis suggested that the association between Ruminococcaceae_UCG_004 and inattention was independent of other (selected) genera. Our results also point towards a potential effect of genera such as Ruminococcus_2 and Ruminococcaceae_uncultured on inattention.To our knowledge, our findings constitute the first (proof-of-concept) of its kind showing the association between the microbiome relative abundance and ADHD symptoms in the adolescent/adult group. Our results showed general differences in microbiota composition (beta-diversity) between the groups as well as differences at the genus level, where the relative abundance of the Ruminococcaceae_UCG_004 association. However, when directly comparing medicated (n = 19) and non-medicated individuals (n = 22) with ADHD, we found two genera being different: Dialister was increased and Phascolarctobacterium was decreased in medicated individuals. While this is not the first time that ADHD medication is examined in terms of an effect on gut microbiota [Importantly, our result indicate that ADHD medication did not have an effect on crobiota , it is tRuminococcaceae family and the genera belonging to this family have shown altered relative abundance in individuals diagnosed with several psychiatric diseases, i.e., autism, bipolar disorder, anxiety, depression, schizophrenia, as well as ADHD [Ruminoccocace_UCG_004 genus and its effect on the host, the Ruminococcaceae family is well described. This family is commonly present in the mammalian intestinal tract, displaying the ability to degrade cellulose and hemicellulose from plant material [Ruminococcus [Ruminococcus_2, became significantly associated with symptoms of inattention in the multivariate analysis  to query for highly similar sequences. The BLAST search revealed that the sequence of the Ruminococacceae_UCG_004 genus was included in Evtepia gabavorous species  [As a post-hoc exploratory analysis we blasted the d (GABA) ,53. Thisd (GABA) ,55. Mored (GABA) .N = 98) and ii) the fact that we were not able to collect information on lifestyle, dietary patterns (including probiotics), or antibiotic use at the time of feces collection. For the former, we applied two QC steps in order to deal with the big number of variables (genera), their expected small effects and big interindividual variation of the gut microbiota. First, we applied an uncorrected non-parametric approach . Second, we applied an outlier detection step, prior to our regression analysis, in order to reduce the chance of false positives/negatives. For the latter, we were only able to collect information on BMI and while we acknowledge that this is not enough to account for the effects of diet and lifestyle, it is encouraging to see that there was no difference between the groups. Moreover, we looked for and removed samples with a very low bacterial diversity (high proportion of zeros) by applying a 10% genus-based frequency cut-off per sample. This can be used as a proxy for those individuals currently using antibiotics which would show a smaller bacterial diversity.This study should be viewed in the context of several strengths and limitations. Our strengths include the use of a sample with high-quality clinical assessment and of age-matched clinically ascertained controls. The limitations of our study are: i) limited sample size  between the ADHD group and controls. We did not replicate this difference in the current study. The reason for the lack of replication between studies could be due to methodological differences. These include DNA extraction [ADHD studies investigating gut microbiota composition show inconsistent results ,43,56,57traction , 16S rRNtraction , bioinfotraction , sample traction ,62. FollRuminococcaceae family (Ruminococcaceae_UCG-004) is associated with inattention severity. Further studies should validate the present findings and identify potential gut-brain mechanisms via genera belonging to the Ruminococcaceae family, such as those related to SCFA production and to alteration of GABA neurotransmitter.In conclusion, our work showed the differences in microbiota composition between individuals with ADHD and controls, and that variation of a genus from the


Empirical research has stated that Attention Deficit Hyperactivity Disorder (ADHD) might underlie intimate partner violence against women (IPVAW) perpetration. Even though there is a clear relationship between these two variables, it is still unknown how ADHD facilitates violence proneness. In this regard, psychophysiological variables such as skin conductance levels (SCL) might offer information about emotional regulation when individuals cope with stress. Furthermore, alexithymia traits might be a strong candidate in explaining the above-mentioned emotional dysregulations. Hence, we compared the SCL response to acute cognitive stress in IPVAW perpetrators with and without ADHD symptoms to that of controls (non-violent and unaffected), and we also assessed the presence of alexithymia traits and their role in emotional regulation. Our data point out that ADHD IPVAW perpetrators presented higher SCL and negative affect than controls, particularly during the recovery period. Moreover, ADHD IPVAW perpetrators showed higher self-reported alexithymia, and this variable was a good predictor of autonomic and psychological state dysregulations, even after controlling for the effects of alcohol and drug misuse. Therefore, our study reinforces the need to consider psychophysiological measurements when screening the therapeutic needs of IPVAW perpetrators, due to their relatively low cost and the significant contents of their results. Finally, we also highlight the key role of alexithymia in this violent population, which should be considered when designing cognitive intervention training coadjutant to current psychotherapies for IPVAW perpetrators. The World Health Organization (WHO) estimates that 35% of women worldwide have been victims of certain types of physical and/or sexual intimate partner violence at some point in their lives. This type of violence exponentially increases the victims’ risk of developing mental health disorders. It is even more worrisome that approximately 38% of assassinated women are murdered by their violent male intimate partner  ThereforAlthough psychologists have assessed IPVAW from cognitive-behavioral approaches in order to develop batterer intervention programs, they have paid little attention to the potential influence of biological variables ,3. Due tIn recent years, empirical research has highlighted that Attention Deficit Hyperactivity Disorder (ADHD) is a risk factor for several types of IPVAW perpetration in young and adult men. In fact, unless ADHD symptoms are treated, the risk of IPVAW perpetration will remain high ,8,9,10. Neuroimaging techniques offer information about brain functioning in ADHD patients, but they are still expensive. Thus, it is advantageous to include peripheral markers of brain functioning because they are cheap and can be easily adapted to different contexts. A peripheral marker of the Autonomic Nervous System (ANS), skin conductance (SC), might offer information about arousal changes in individuals. SC is a good marker of ANS functioning because the sympathetic nervous system innervates sweat glands, and so changes in emotional arousal might be easily captured through SC by registering electrodermal activation ,12. In tMost individuals with ADHD tend to present autonomic arousal dysregulations in coping with acute stress . SeveralRegarding autonomic regulation in IPVAW perpetrators, it was previously concluded that they presented a heightened SC after recovering from an acute laboratory stressor. In other words, in the period when parasympathetic activation has to increase in order to return to initial allostatic state values, IPVAW perpetrators’ sympathetic systems remain activated . Later, With this in mind, in this study we first aim to determine whether IPVAW perpetrators with and without ADHD symptoms have different SCL and psychological state responses to acute laboratory stress from non-violent men. As previously demonstrated with ADHD individuals ,19,20,21The final sample was composed of 55 healthy male volunteers (36 IPVAW perpetrators and 19 controls). All of the IPVAW perpetrators came from the CONTEXTO psycho-educational and community-based treatment program at the University of Valencia (Spain). These men received a prison sentence of less than two years, and they did not have previous criminal records. Thus, their sentence was suspended on the condition that they attend the CONTEXTO program and complete the program in about one year . Regarding mood states, significant effects of the “time” and the “time x group” interaction were found  = 5.68, controls . Moreovep = 0.004) and 15.1% of the variance in the SCL recovery period  = 10.62, p = 0.002). After including covariates, the TAS-20 still significantly predicted both variables  = 3.05, p = 0.028, 95% CI = 0.04 to 0.61; and β = 0.364, t (55) = 2.34, p = 0.023, 95% CI = 0.05 to 0.60; respectively). Nonetheless, alexithymia traits did not significantly predict the rest of the SCL periods of SCL AUCi.The TAS-20 predicted 13.3% of the SCL resting period  = 9.28, p = 0.008), 12.8% of the variance in the difference between anger state post-task minus baseline  = 8.93, p = 0.004), and 6% of the variance in the mood state post-task minus baseline  = 4.37, p = 0.041). After including covariates in step 2, the TAS-20 did not reach statistical significance. Regarding psychological variables, the TAS-20 predicted 10.8% of the variance in the anger state post-task  = 7.56, Even though all of the participants showed a similar pattern of SCL changes in response to acute stress, ADHD IPVAW perpetrators had higher SCL than controls, particularly during the recovery period. Regarding the psychological response, a differential pattern of response was found. Indeed, IPVAW perpetrators (both groups) presented an increased anger state after the task and a marked worsening of mood, whereas controls showed a decrease in anger and less worsening of mood. Finally, although alexithymia traits were higher in ADHD IPVAW perpetrators than in the rest of the groups, alexithymia was a good predictor of autonomic and psychological state dysregulations in all the groups, even after controlling the effect of drug misuse. Contrary to our expectations in the first hypothesis, there were no differences in the general pattern of response from resting period to task in the SCL between groups. Nonetheless, we cannot conclude that ADHD IPVAW perpetrators’ SCL response to acute stress was exactly the same as that of the rest of the groups because the SCL of ADHD IPVAW perpetrators did not decrease after the task or during the recovery period, but it did in the rest of the groups (non-ADHD IPVAW perpetrators and controls). Therefore, the heightened autonomic levels during the recovery period seem to be key to reinforcing the autonomic dysregulation  in adults with ADHD , with thAs expected, ADHD IPVAW perpetrators showed a differential psychological state response to stress in comparison with the rest of the groups, particularly controls. Indeed, they experienced a significant increase in negative affect (high anger and worsening of mood), whereas controls experienced a slight decrease in anger and worsening of mood. This increase in negative affect might be a response indicating difficulties dealing with inner states, thus affecting the way of coping satisfactorily with stress. Hence, as a second aim in our study, we proposed that the differential autonomic and psychological state responses would be explained by high alexithymia traits. In this case, our data pointed out that (1) ADHD IPVAW perpetrators presented the highest self-reported levels of alexithymia, and (2) the higher the alexithymia traits, the higher the heightened SCL response during the resting and recovery periods, as well as the increase in negative affect (high anger and worse mood). Moreover, it is important to keep in mind that we included group and drug misuse as covariates in the statistical analyses, but alexithymia still predicted autonomic and emotional dysregulations, which reinforces alexithymia’s importance in this population. Therefore, this result highlights that we need to pay attention to alexithymia when designing future batterer intervention programs ,34. Nowadays, there is no clear and specific treatment to exclusively treat alexithymia, even though it has been pointed out that it could be improved by providing training in socio-cognitive variables closely related to it ,51,52, fThis is the first study to point to heightened arousal of IPVAW perpetrators with ADHD symptoms in response to an acute laboratory stressor, but we recognize that the experiment has some limitations that should be considered in future studies. Nevertheless, this pilot study is part of an ongoing research effort to improve our understanding of the main variables underlying IPVAW perpetration. The research design is strong, including a control group that was matched on the main demographic characteristics and a reliable measurement of arousal changes such as SCL. First, the major limitations of the study are the small sample size and its correlational nature. For this reason, future studies should explore group differences in larger samples. Another limitation that should be highlighted is the absence of cardiorespiratory markers  and sympathetic  and parasympathetic (respiratory sinus arrhythmia) indicators, which could help to increase the value of our data. Furthermore, it would also be interesting to consider other biomarkers, such as serum cytokines, alpha amylase, testosterone, cortisol, and oxytocin to assess emotional regulation. Lastly, and most importantly, we assessed ADHD symptoms based on performance on the CPT-III and the score on a self-report, but it would be necessary to cross these data with a psychotherapist’s diagnosis of ADHD based on DSM-5 criteria. In fact, based on performance on this test, we can only have a suspicion of this disorder, and not a real diagnosis. However, the CPT-III provides a strong and reliable measurement of ADHD . In summary, our study reveals that IPVAW perpetrators with ADHD present a probable sympathethic/parasympathethic imbalance when coping with stress that is partly explained by alexithymia. For this reason, we strongly suggest that it is necessary to consider psychophysiological measurements for screening the therapeutic needs of IPVAW perpetrators, due to their relatively low cost and the significant content of their results. In fact, these peripheral markers offer information about the central nervous system (CNS), specifically, how individuals regulate their emotions and behavior. Therefore, we can employ it to build risk profiles of IPVAW perpetrators or other violent individuals. Furthermore, our study reinforces the need to consider alexithymia as a key variable in designing cognitive training for IPVAW perpetrators. Finally, all our results shed light on the biological functioning underlying violence proneness and on the different subtypes of IPVAW perpetrator with differential therapeutic needs.