F = 30.24; p < 0.01), with only maternal ADHD  and depressive symptoms  being uniquely significant. Maternal ADHD symptoms did not moderate the relationship between child ADHD symptoms and maternal perceived chronic stress  = − 0.05; p = 0.96). Mother’s age became the only significant predictor of maternal HCC . Based on these findings, practitioners are advised to be aware of and take into account possible maternal ADHD and depressive symptomatology and perceived chronic stress when treating children diagnosed with ADHD.Providing care for a child with attention-deficit/hyperactivity disorder (ADHD) is associated with parenting stress. Moreover, adults with elevated ADHD symptoms report increased perceived stress. Despite this, it has rarely been examined whether and how child and maternal ADHD symptoms may affect maternal perceived stress and the stress-sensitive hypothalamic–pituitary–adrenal axis. This study therefore investigated the possible impact of child and maternal ADHD symptoms on mothers' perceived chronic stress and hair cortisol concentration (HCC), while simultaneously considering the effects of child oppositional defiant/conduct disorder (ODD/CD) and maternal depressive symptomatology. In total, 124 mothers (35.96 ± 5.21 years) of preschool children were included. Maternal perceived stress, ADHD and depressive symptoms were assessed using self-report measures. Child ADHD symptoms were assessed using an interview and questionnaires completed by mothers and teachers. Additionally, mothers provided information about their children’s ODD/CD symptoms. Hair samples were taken from mothers to assess HCC. Child and maternal ADHD, child ODD/CD, and maternal depressive symptoms accounted for 50% of the variance in perceived chronic stress (The online version contains supplementary material available at 10.1007/s00702-021-02377-1. Providing care for a significant other with special needs, such as caring for a relative with dementia  Child and maternal ADHD symptoms predict maternal perceived chronic stress when controlling for child ODD/CD and maternal depressive symptoms; (2) The relationship between child ADHD symptoms and maternal perceived chronic stress is moderated by maternal ADHD symptoms; (3) Child and maternal ADHD symptoms predict maternal HCC when controlling for child ODD/CD and maternal depressive symptoms.The present study draws on data from the first assessment wave of an ongoing longitudinal study  = 0.01; p = 0.20), educational level of mother (χ2 (5) = 5.36; p = 0.37), educational level of father (χ2 (3) = 4.63; p = 0.92), age of mother (t(193) = 0.95; p = 0.34), age of child (t(195) = − 0.87; p = 0.38), child ADHD rating scale parent rating (t(121.72) = 0.1; p = 0.99), child ADHD rating scale teacher rating (t(192) = − 0.48; p = 0.63), child ADHD symptoms clinical interview (t(189) = 0.27; p = 0.79), child ODD/CD symptoms (t(121.25) = 0.48; p = 0.64), and depressive symptoms of mother (t(188) = 1.84; p = 0.07). However, there was a significant difference with respect to maternal ADHD symptoms, as indicated by both the Conners’ Adult ADHD Rating Scale (t(188) = 2.33; p = 0.02) and the Wender–Reimherr Interview for adults (t(194) = 2.39; p = 0.02), with included mothers scoring higher  than mothers who were excluded from the analyses due to incomplete data . Thus, there was a higher probability of missing data for mothers with lower ADHD symptoms.The total sample consisted of 198 parent–child dyads. Since only seven fathers participated in the data collection, fathers and their children had to be excluded from the analyses to prevent potential gender bias. A dyad with a grandmother as a primary caregiver was also removed from the sample. A further 65 dyads had to be excluded due to missing data and an additional dyad was excluded because of an extreme outlier (SD = 10.31) on the HCC variable   and can reliably differentiate between children with and without ADHD  Daley  was condS) Daley  for presADHD rating scale   and good validity (distinction between children with and without ADHD) (Breuer and Döpfner r = 0.34 (p < 0.01); FBB-ADHS-V parent and Pre-PACS: r = 0.45 (p < 0.01); FBB-ADHS-V teacher and Pre-PACS: r = 0.16 (p = 0.07). A composite ADHD child symptom score, identical to that used by Pauli-Pott et al. .In addition, the preschool version of the t et al. , was creChild ODD/CD symptoms at the age of 4–5 years were assessed with the German FBB-SSV questionnaire    and differentiates reliably between adults with and without ADHD  and can differentiate well between adults with and without ADHD . Analogous to the computation of the child’s ADHD score, a composite ADHD score was also created for mothers by summing up the z-transformed scores of the two scales. The composite score showed good internal reliability (α = 0.78).The two scales intercorrelated significantly (α = 0.89) and validity (correlations with other depression questionnaires) have been established   .The Mothers’ HCC was assessed using hair samples. Several thin hair strands were cut from the posterior vertex region of the head as close as possible to the scalp. HCC was determined using the first proximal three centimetres closest to the scalp, which is thought to reflect the cumulative cortisol secretion of the past three months Wennig . The HCCPreliminary analyses using bivariate correlation analyses with bootstrap confidence intervals were conducted in preparation for major analyses and to examine relationships among demographic variables , predictor variables and the two outcome variables. None of the demographic variables was related to perceived chronic stress. As log-transformed HCC correlated significantly with mother’s age, it was controlled for in the analysis of hypothesis 3.To analyse hypothesis 1, a hierarchical multiple regression analysis was conducted with mothers’ perceived chronic stress as an outcome variable. Child and maternal ADHD symptoms were entered in step 1 and child ODD/CD and maternal depressive symptoms in step 2.Hypothesis 2 was tested with a moderation analysis using the SPSS macro PROCESS Hayes, . MaternaHypothesis 3 was tested with a hierarchical regression analysis, with HCC as an outcome variable. HCC showed an extremely skewed distribution. An extreme outlier (SD = 10.31) was excluded, and log-transformation was executed to normalise the distribution. Child and maternal ADHD symptoms were entered in the first step, and child ODD/CD, maternal depressive symptoms, and mother’s age were entered in the second step.For all three regression models, normality of residuals, linearity, multicollinearity, homoscedasticity, and the absence of influential outliers were confirmed.Intercorrelations among study variables are depicted in Table F = 30.24; p < 0.01) and accounted for 50% of the variance in maternal perceived chronic stress. Regression coefficients are depicted in Table R2 = 0.31; F = 27.15; p < 0.01), but only the effect of maternal ADHD symptoms was uniquely significant . The introduction of child ODD/CD and depressive symptoms significantly improved the prediction of maternal perceived chronic stress  = 23.31; p < 0.01). Only the contribution of maternal depressive symptoms was uniquely significant . For partial regression plots see supplementary information.The first regression model, examining the impact of child and maternal ADHD symptoms on maternal perceived chronic stress while controlling for child ODD/CD and maternal depressive symptoms, was significant  = − 0.05; p = 0.96), indicating no significant moderation effect.The moderation term of the analysis exploring the moderating role of maternal ADHD symptoms in the association between reported child ADHD symptoms and maternal perceived chronic stress was not significant  = 2.73; p = 0.02) and accounted for 10% of the variance in mother’s HCC . The positive β-value suggests that mothers’ HCC increased with age.The third regression model, predicting mother’s HCC by child and maternal ADHD symptoms while controlling for child ODD/CD and maternal depressive symptoms as well as mother’s age, reached significance . Older individuals typically have poorer health   affect maternal stress. Future studies are needed to examine the role of these contextual variables.A general limitation of this study lies in its correlational nature, making it impossible to infer causal associations. Longitudinal studies could be able to clarify this matter.The present study goes some way towards improving our understanding of the factors influencing chronic stress in mothers of children with elevated ADHD symptoms. Furthermore, the study is the first to investigate the similarity-fit/misfit process in the context of chronic stress.Taken together our findings add to the literature by revealing that maternal own ADHD and depressive symptoms significantly contribute to self-reported stress, while child’s ADHD and ODD/CD symptomatology may play only a minor role in these vulnerable mothers. Furthermore, the relationship between child ADHD symptoms and maternal chronic stress did not change with increasing maternal ADHD symptoms. These findings from self-report measures were not supported by a biological stress measure.Thus, treating children with ADHD should include heightened awareness for subthreshold depression and ADHD related problems of mothers, by either referring them to an adult specialist or revising treatment manuals for children with ADHD including work with parents going beyond the honing parenting skills (e.g. stress management).Supplementary file1 (DOCX 238 kb)Below is the link to the electronic supplementary material.


Drosophila PGRP-LB belongs to the amidase PGRPs and downregulates the immune deficiency (IMD) pathway by cleaving meso-2,6-diaminopimelic (meso-DAP or DAP)-type PGN. While the recognition process is well analyzed for the non-catalytic PGRPs, little is known about the enzymatic mechanism for the amidase PGRPs, despite their essential function in immune homeostasis. Here, we analyzed the specific activity of different isoforms of Drosophila PGRP-LB towards various PGN substrates to understand their specificity and role in Drosophila immunity. We show that these isoforms have similar activity towards the different compounds. To analyze the mechanism of the amidase activity, we performed site directed mutagenesis and solved the X-ray structures of wild-type Drosophila PGRP-LB and its mutants, with one of these structures presenting a protein complexed with the tracheal cytotoxin (TCT), a muropeptide derived from the PGN. Only the Y78F mutation abolished the PGN cleavage while other mutations reduced the activity solely. Together, our findings suggest the dynamic role of the residue Y78 in the amidase mechanism by nucleophilic attack through a water molecule to the carbonyl group of the amide function destabilized by Zn2+.Peptidoglycan recognition proteins (PGRPs) are ubiquitous among animals and play pivotal functions in insect immunity. Non-catalytic PGRPs are involved in the activation of immune pathways by binding to the peptidoglycan (PGN), whereas amidase PGRPs are capable of cleaving the PGN into non-immunogenic compounds.  PGN ibacteria ,2. PGN, bacteria ,4,5. SomBombyx mori [Drosophila melanogaster genome contains 13 PGRP genes encoding over 20 different proteins [Drosophila, PGRP-LC and PGRP-LE recognize DAP-type PGN and activate the IMD pathway [The first PGRP was described in the silkworm byx mori ; furtherbyx mori . Insect byx mori . The fruproteins ,9 that aproteins . In Dros pathway , while P pathway .Drosophila PGRP-LB [Drosophila PGRP-LB is specific to the DAP-type PGN. Its expression is regulated at the transcriptional level by the IMD pathway. This IMD-regulated expression of PGRP-LB in the gut avoids a constitutive local immune response to microbiota bacteria, and creates a threshold of infection for a systemic immune response activation [Drosophila PGRP-LB gene encodes three isoforms, two cytosolic (PGRP-LBPA and PGRP-LBPD) and one secreted (PGRP-LBPC) [PC and cytoplasmic isoform PGRP-LBPA are the same protein (PGRP-LBPA/PC), and following the signal peptide of PGRP-LBPC, they share the same sequence  . The secsequence A. The sefunction A. It waserocytes . In inseerocytes ,19, and erocytes .2+ in only amidase PGRPs, which is coordinated by two histidines and one cysteine. Previous non-catalytic PGRP structures have provided a more comprehensive view on the residues responsible for the specificity towards the two types of PGN -tetraDAP  substrate while remaining very active against other muropeptides and polymeric DAP-type PGN. Therefore, the presence of the longer N-terminal in PGRP-LBPD did not seem to induce any specificity for a particular compound leading to any influence in activity.ompounds ,16. Moreompounds . The actty assay . Both PGBranchiostoma belcheri tsingtauense, where the PGRP domain is fused with a chitin binding domain [PA/PC isoform, described as PGRP-LB in previous papers [This extra sequence could bring a supplementary domain complementing the protein activity of PGN degradation, similar to the PGRP3 from g domain . Howevers papers ,21.Drosophila PGRP-LB [Drosophila PGRP-LBPA/PC histidine 42 (H42), histidine 152 (H152), tyrosine 78 (Y78) and cysteine 160 (C160) in coordination with Zn2+ in the catalytic site  only the tyrosine is involved in the amidase reaction and no stabilization of the transition state is required.Because no other residues seemed close enough to be involved the catalytic reaction or in the stabilization of the transition state during the enzymatic reaction, we raised two alternative hypotheses: (a) one of the ZnDrosophila PRGP-LBPA/PC: H42A, Y78F, H152A and C160S.To tackle these two scenarios, we designed and analyzed four different mutations of the conserved residues in the catalytic Drosophila PGRP-LB   [The structure of the DB 7NSX) . The res in 7NSX . This innd Zn2+) .PA/PC were solved by X-ray crystallography   . UnfortuPC_C160S B–D. PGRPith Zn2+ . Howevera serine C. Additicrystals E–G. We ssolution C, while 2+ to the reaction buffer used for activity assays   . In thisDB 7NT0) A.PA/PC to the PGN is ensured by the recognition of the meso-DAP residue, interacting via hydrogen bonds with R92 (Drosophila PGRP-SA [Drosophila PGRP-LBPA/PC for the DAP-type PGN is confirmed with an activity around 30 times higher for the TCT compared to the GM(anh)-pentaLys  is accommodating the sugar moiety even though no interpretable electron density is visible in the vicinity.The absence of the electron density for the carbohydrate moiety of the TCT in the PGRP-LBPA/PC _Y78F structure complexed with TCT allows to better understand the PGN binding and multimerization event in the non-catalytic PGRPs in Drosophila. PGRP-LC and PGRP-LE are PGN receptors involved in the activation of the IMD pathway [PA/PC_Y78F + TCT structure . The genes were cloned into the pPOPINM plasmid (PPUK) [E. coli DH5alpha  for plasmid amplification and subsequently into SHuffle® T7 Express Competent E. coli cells  for protein expression. Cultured cells were grown in LB broth with ampicillin at 30 °C and induced at A600 of 0.8 by adding IPTG to a final concentration of 1 mM for overnight expression at 16 °C.The gene segment coding for the different isoforms of d (PPUK)  using NE® centrifugal concentrators .The bacterial pellet was lysed by sonication in 100 mM Trizma base pH 7.5, 500 mM NaCl and 20 mM Imidazole. The supernatant was then applied on a HisTrap HP 5 mL column  and washed with the same buffer as the lysis. The protein was then eluted by increasing the imidazole up to 250 mM. The eluted fraction was overnight dialyzed in 100 mM Trizma base pH 7.5, 500 mM NaCl and 20 mM Imidazole with 3C Protease his-tagged, in order to remove the His-MBP tag. Further purification step was done by applying the protein again on the HisTrap column, the protein is then collected in the flow-through. Finally, a gel filtration is done on a HiPrep Sephacryl S-100 HR  in 20 mM HEPES pH 7.5 and 150 mM NaCl. The proteins were concentrated with AmiconThe protein purification quality was assessed by 4–12% SDS-PAGE .E. coli mutant strain BW25113 Δ6LDTs::KanR that does not express all six L,D-transpeptidase genes. Culture was grown overnight at 37 °C in LB broth with kanamycin. The bacterial pellet was resuspended in a minimal volume of 0.9% NaCl and poured drop by drop in 100 mL of 4.5% SDS at 95–100 °C. After 1 h of incubation, the mixture was cooled down overnight at room temperature. The suspension was centrifuged for 20 min at 200,000× g and the pellet was washed several times with water. Finally, the peptidoglycan was resuspended in a minimal volume of ultrapure water. All the enzymatic reactions described in the The preparations of the different PGN compounds were done as described before . To sum Lys compound was generated from the M. luteus PGN by following the same process.The GM(anh)-pentaThe different purified compounds are represented in 2, pure polymeric PGN or PGN fragment (0.2 mM) and the protein . The reaction was left to incubate for 30 min at 37 °C, then 2 μL of phosphoric acid are added to stop the reaction. The decrease of substrate and the apparition of products were measured by HPLC to calculate the specific activity of the various proteins.The reaction mixture (50 μL) contains 20 mM HEPES pH 7.5, 1 mM ZnClE. coli was incubated with PGRP-LB or its mutants in 20 mM HEPES pH 7.5 and 150 mM NaCl for 1 h at 4 °C. The samples were centrifuged at 16,000× g for 10 min and the supernatant fractions were collected representing the unbound fractions. PGN pellets were washed with the same buffer and then dissolved in Leammli buffer representing the bound fractions. The samples were then analyzed by 10% SDS-PAGE.The method was already described for the PGRP-LB . PolymerTMX plates  at 20 °C, 100 nL of protein mixture were mixed with 100 nL of crystallization buffer using a Mosquito. The crystallization buffer varied for the different proteins, PGRP-LB wild-type , PGRP-LB_C160S , PGRP-LB_Y78F  and PGRP-LB_Y78F + TCT . The crystals were cryo-protected adding glycerol to a concentration of 25%. Data were collected on I24 and I04 .The proteins were concentrated at 10 mg/mL in 20 mM HEPES pH 7.5 and 150 mM NaCl. For the TCT complexed structure, the muropeptide was added to a final concentration of 1 mM. The crystallization was performed by the sitting-drop vapor-diffusion method in 96-well CrystalQuickCoot [Diffraction data sets were indexed and integrated with DIALS . The resCoot  and refiCoot ,50. CrysCoot .The X-ray emission fluorescence spectrum were analyzed with pyMCA . The ligThe TCT was build using the geometry of the tetrapeptide from our PGRP-LB_Y78F + TCT structure and the geometry of the sugar from the PGRP-LC and PGRP-LE complexed to TCT structures (PDB 2F2L & 2CB3) ,24. The


Objective: Our objective was to examine developmental trajectories of co-occurring psychopathology symptoms from childhood to young adulthood in individuals with Attention-Deficit/Hyperactivity Disorder (ADHD), individuals with Autism Spectrum Disorder (ASD), and typically developing (TD) individuals.Method: We assessed co-occurring psychopathology symptoms in 61 individuals with ADHD, 26 with ASD, and 40 TD individuals at baseline , 2-year follow up , and 10-year follow up . We analyzed trajectories of internalizing behaviors, externalizing behaviors, and total problems with linear mixed models.Results: From T1 to T3, the ADHD group displayed a small decline in internalizing behaviors (d = −0.49) and large declines in externalizing behaviors (d = −0.78) and total problems (d = −0.71). The ASD group displayed large declines in internalizing behaviors (d = −0.79), externalizing behaviors (d = −0.80), and total problems (d = −0.89). From T1 to T2, the decline in externalizing behaviors and total problems were significantly smaller in the ADHD group compared with the ASD group. The ADHD and the ASD group displayed more co-occurring symptoms compared with the TD group at T3.Conclusion: Individuals with ADHD and ASD, respectively, displayed declines in co-occurring symptoms from childhood to young adulthood. Individuals with ASD displayed an earlier decline compared with individuals with ADHD. Compared with TD individuals, individuals with ADHD and ASD, respectively, continued to display elevated levels of co-occurring symptoms in young adulthood. Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two major neurodevelopmental disorders –3. DespiThe co-occurring symptoms may be of particular importance, as these symptoms predict poorer adult outcome, lower occupational functioning, and impaired quality of life in individuals with ADHD and individuals with ASD –24. Two d = 0.94–1.69) (d = 0.87–1.29) (d = 0.77–1.30)  , adolesc87–1.29) , and youA few studies have examined the developmental trajectories of co-occurring symptoms in individuals with ADHD. In a previous study by our research group, declines in internalizing and externalizing behaviors over a 2-year period (mean age 11–13) were found . Howevern = 19)  . Thus, tHigh levels of co-occurring symptoms are found across the lifespan in individuals with ASD , 43, 44.A few studies investigating the developmental trajectories of co-occurring symptoms form childhood to young adulthood in individuals with ASD have been conducted. Two studies have found declining trajectories of internalizing and externalizing behaviors from childhood  to young adulthood  in individuals with ASD , 47. In One limitation with the four aforementioned longitudinal studies were the inclusion of individuals with ASD both with and without intellectual disability. In fact, in all four cohorts the majority of participants have been reported to have an intellectual disability (IQ < 70) , 50, 51.To our knowledge, no study has previously compared the developmental trajectories of co-occurring symptoms from childhood to young adulthood in individuals with ADHD and individuals with ASD. Further, only one previous study (with few participants) have examined the trajectories of co-occurring symptoms in individuals with ADHD from childhood to young adulthood , and no We aimed to examine and compare the developmental trajectories of co-occurring symptoms among individuals with ADHD, individuals with ASD, and TD individuals from childhood to young adulthood. In line with the majority of previous studies, we hypothesized that individuals with ADHD and individuals with ASD would display declining trajectories of co-occurring symptoms over time , 45–47. This study is part of the Lillehammer Neurodevelopmental Follow-up Study (LINEUP), a longitudinal study currently spanning baseline assessment (T1), 2-year follow-up (T2), and 10-year follow-up (T3). At T1, 85 children with ADHD, 38 children with ASD, and 50 TD children participated. Participants were recruited from child and adolescent psychiatric outpatient clinics at Innlandet Hospital, Norway, upon consecutive referrals. All individuals between 8 and 17 years of age referred for assessment of ADHD or ASD were invited to participate in the study. Those agreeing to participate were assessed for ADHD or ASD and included in the study if they meet diagnostic criteria based on DSM-IV. Thus, age at T1 equals the time of diagnosis in our sample. TD individuals were recruited through local schools and had to attend regular classes. At T1, all TD individuals were screened for mental disorders with the Kiddie-Schedule for Affective Disorders and Schizophrenia/Present and lifetime version (Kiddie-SADS), conducted separately with participants and their parents. Exclusion criteria for all participants were prematurity (<36 weeks), having a disease affecting the central nervous system, or having IQ < 70. An additional exclusion criterion for the ADHD group was no history of stimulant treatment. Additional criteria for the TD group were no history of psychiatric disorder, dyslexia or head injury with loss of consciousness. Demographic and clinical characteristics of all three groups are presented in Diagnostic assessment at T1 was based on a semi-structured clinical interview (Kiddie-SADS)  conducteParticipants with ADHD and ASD received treatment as usual (TAU) at the child and adolescent psychiatric outpatient clinic after diagnosis. TAU could include psychoeducation for parents, medical treatment, and in some instances unstructured social skills training (for the ASD group). Participants were generally referred for treatment whiteout explicit focus on co-occurring symptoms, and thus, clinical focus may have centered on core symptoms and school performances and less on co-occurring symptoms . At T2, p ≥ 0.013).Two- and 10-years after baseline assessment, all participants were invited for follow-up assessment. Out of the 173 original participants, 168 participated at T2, giving an overall retention rate of 97.1%  , 61 as aT-scores  based on American norms. Norwegian norms does not exist, but Norwegian children generally seems to obtain lower T-scores compared with American children  , a parenchildren . The psychildren –65.At T3, we used the ASEBA scale, Adult Self-Report (ASR) , a self-At T1 and T2, the ARS-IV  was usedAt T1 and T2, the ASSQ  was usedp-values were below 0.004 (0.05/13 = 0.004).We used SPSS version 26 for all statistical analyses. We used an alpha-level of 0.05 for statistical significance and used Bonferroni corrected alpha-levels for multiple comparisons. To examine differences in demographic and clinical variables between groups, we used analysis of variance (ANOVA) and chi-square test of independence. Differences between the ADHD, ASD, and TD groups were considered significant if r, to examine associations between changes in core symptoms (ADHD and ASD) and changes in co-occurring symptoms from T1 to T2.To examine the developmental trajectories of co-occurring symptoms and relate changes over time to group affiliation, we used Linear Mixed Models (LMM) for longitudinal analyses. The technique includes participants with partial data and is thus more robust in longitudinal studies where missing data is common , 72. We post-hoc tests.To examine group differences in outcome , we used multivariate analysis of variance (MANOVA) with the three ASEBA scales internalizing behaviors, externalizing behaviors, and total problems as dependent variables. A significant group effect (Wilks Lambda) was followed-up with ANOVAs for each dependent variable and Bonferroni p = 0.025) in externalizing behaviors relative to the TD group, whereas the ASD group displayed significant declines in both internalizing behaviors (p = 0.036), externalizing behaviors (p < 0.001), and total problems (p = 0.001) relative to the TD group. The declines in externalizing behaviors  and total problems  were significantly larger in the ASD group compared with the ADHD group.p = 0.007), externalizing behaviors (p = 0.004), and total problems (p < 0.001) relative to the TD group. The ASD group continued to display declines in internalizing behaviors (p = 0.022) and total problems (p = 0.007) relative to the TD group, but not externalizing behaviors. In contrast, internalizing behaviors (p = 0.028) and total problems (p = 0.001) increased significantly in the TD group.From T2 to T3, the ADHD group displayed significant declines in internalizing behaviors (d = −0.49) decline in internalizing behaviors and medium to large declines in externalizing behaviors (d = −0.79) and total problems (d = −0.71). The ASD group displayed large declines in both internalizing behaviors (d = −0.79), externalizing behaviors (d = −0.80), and total problems (d = −0.89). From T1 to T2, the decline in externalizing behaviors in the ADHD group correlated significantly with a decline in ADHD symptoms  and the declines in internalizing  and externalizing behaviors  in the ASD group correlated significantly with a decline in ASD symptoms. The decline in total problems in the ASD group correlated non-significantly with a decline in ASD symptoms .In total from T1 to T3, the ADHD group displayed a small to medium  = 4.584, p < 0.001, post-hoc tests showed that the ADHD  and the ASD  group displayed significantly more internalizing behaviors compared with the TD group. For externalizing difficulties, post-hoc tests showed that the ADHD group displayed significantly more externalizing behaviors compared with the TD group , whereas the ASD group displayed non-significantly more externalizing behaviors compared with the TD group . For total problems, post-hoc tests showed that the ADHD  and the ASD  group displayed significantly more total problems compared with the TD group. There were no significant differences between the ADHD and the ASD group.A MANOVA with ASEBA scales at T3 as dependent variables showed a significant effect of group [ains see . For intOur findings provide the field with at least three new pieces of knowledge. First, we provide evidence showing declining trajectories of co-occurring symptoms across three assessment waves from childhood to young adulthood (mean age 11–21) in individuals with ADHD. Second, we provide evidence suggesting similar but partially differential trajectories of co-occurring symptoms among individuals with ADHD and individuals with ASD. Third, we found that individuals with ADHD and individuals with ASD continue to display more co-occurring symptoms in young adulthood compared with TD individuals, despite differential developmental trajectories in the three groups (declining vs. increasing). Overall, our findings highlight the persistence of co-occurring symptoms among individuals with ADHD and ASD, respectively, while at the same time providing some optimism regarding the symptom trajectories.The decline in co-occurring symptoms among individuals with ADHD is in accordance with our hypothesis, previous studies , and is Similar to the ADHD group, and in accordance with our hypothesis, we found declines in co-occurring symptoms across all domains for the ASD group. This is consistent with two of the previous studies of individuals with ASD , 47. OurIn contrast to ASD, we speculate on whether the co-occurring symptoms among children with ADHD may be closer linked to the core symptoms  and thatA finding that was particularly notable when comparing the developmental trajectories of the ADHD and ASD group with that of the TD group, was that the ADHD and the ASD group displayed declines in co-occurring symptoms from T2 to T3 (mean age 13–21), a period when the TD group displayed increases in internalizing behaviors and total problems. This suggests that while researchers and clinicians have been worrying that individuals with ADHD and ASD may be particularly vulnerable during the transitional periods of adolescence and young adulthood , 90, theAnother explanation may be that individuals with ADHD and individuals with ASD have an illusory self-perception , 94. SomT-score) at T3, suggesting a low load of co-occurring symptoms relative to the US general population. However, previous studies have suggested that Norway is a low scoring country where the population mean is well below 50  ascertained after careful diagnostic assessment including clinical interview, self-, parent-, and teacher-report of social, emotional, and academic functioning, and neuropsychological tests . To our d = 0.64–80), and externalizing behaviors showed a similar developmental trajectory as internalizing behaviors and total problems.Despite the study's notable strengths, some limitations should be mentioned. One limitation is the use of parent-report at T1 and T2 and self-report at T3. Previous studies of individuals with ADHD or ASD have suggested that young adults may be underreporting externalizing behaviors compared with parental reports , 97, whiper se . Since individuals with ADHD seems to have a somewhat slower decline in co-occurring symptoms from childhood to adolescence compared with individuals with ASD, interventions targeting co-occurring symptoms in children and adolescents with ADHD may be especially important to expedite declining trajectories. It is also important that clinicians, parents, and teachers are aware of the high levels of co-occurring symptoms among these individuals, and the persistence of these symptoms over time. Parents may take comfort in knowing that co-occurring symptoms wane over time, but declining trajectories should not be used by clinicians as an excuse not to provide interventions.erik.winther.skogli@sykehuset-innlandet.no.The datasets presented in this article are not readily available because the data serving as the basis for the article submitted is stored in a secured repository at Innlandet Hospital Trust (Norway). Due to ethical restrictions on access to the data pursuant to the consent statements participants signed upon collecting the data, the authors are not permitted to upload a data set to sites outside of the repository. Access to the data, however, is available upon request to all serious researchers by contacting the following persons at Innlandet Hospital Trust: Erik Winther Skogli. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by Regional Committee for Medical Research Ethics in Eastern Norway. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.SO contributed to the conceptualization, data curation, formal analysis, and wrote the original draft. MØ contributed to the methodology, investigation, funding acquisition, supervision, and writing—reviewing and editing. IF contributed to investigation and data curation. PA contributed to methodology, investigation, supervision, and writing—reviewing and editing. ES contributed to conceptualization, methodology, investigation, formal analysis, funding acquisition, supervision, project administration, and writing—reviewing and editing. All authors approved the final manuscript for submission.This work was supported by grants from Innlandet Hospital Trust  and from NevSom, Department of Rare Disorders and Disabilities, Oslo University Hospital (Grant Number: 150616).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Children with early onset of Callous-Unemotional (CU) traits are at a higher risk for long-term, persistent psychosocial problems. The current study aimed to explore the characteristics of CU in preschool children with Attention Deficit Hyperactivity Disorder (ADHD) and the diagnostic significance of CU traits in ADHD.A total of 176 preschool children  aged 4–5 years old were recruited to the study. The participants were assessed for CU traits, emotional and behavioral problems, and how their executive functioning was associated with ADHD using multiple assessment scales. Multiple linear regression analysis was performed to assess the incremental validity of the Inventory of Callous-Unemotional Traits (ICU), adjusting for possible covariates by child’s sex, conduct problems, and oppositional defiant symptoms.F = 30.12, P < 0.001). In terms of callousness, ADHD + Oppositional Defiant Disorder (ODD) group showed a significant high score, and the ADHD only group scored significantly higher than the TDC group . The ICU was negatively associated with the prosocial behaviour subscale  and showed low to moderate positive correlations with emotional and behavioural problems, as well as executive function . The ICU scores explained 6% of the incremental validity in ADHD symptoms. The diagnostic value of the ICU for ADHD was medium and acceptable.The results showed that there was a significant difference of ICU scores between the ADHD and TDC groups (The current study indicated that early identification of CU traits may help clinicians better understand symptoms and behavioural problems in children with ADHD. CU traits therefore could be considered as a useful assessment tool for ADHD. Callous can be defined as lacking empathy and having a shallow affect , which iAttention-Deficit Hyperactivity Disorder (ADHD) is one of the most common psychiatric disorders, affecting approximately 5% of children and adolescents around the world . OppositAs mentioned it is well-documented that CU traits can increase the risk of antisocial behaviour and other psychopathy, such as ODD and CD , 11. ImpStudies in this area have found that adolescents who have CD with co-occurring CU traits display elevated levels of emotional and behavioural dysregulation . NothingLooking beyond examining CU traits and emotional and behavioral problems, researchers also need to pay close attention to the role of other salient factors in early childhood that are linked to ADHD across development. In particular, executive function (EF), a construct that contains cognitive flexibility, working memory, inhibitory control, and emotional control for the purposes of planning and executing goal-directed activity –23, is cDue to the early onset of ADHD and the high co-morbidity of ODD/CD, CU traits should be considered as one of the dimensions when evaluating preschool children with ADHD. Children with ADHD often exhibit external and internal behavioural problems, and EF deficits. Empirical data support these findings, as children who experience higher levels of CU traits show an increase in their conduct problems over time and they are more likely to develop ODD or CD . Thus, iTaken together, the aims of the current study are: (1) To determine whether CU traits can be used to discriminate ADHD from TDC, as well as to identify any differences in the characteristics of CU between ADHD and ADHD + ODD; (2) To examine the extent to which EF and EBPs are associated with CU traits; (3) To assess incremental validity of the ICU scale for predicting ADHD symptoms and the specificity and sensitivity of the ICU in the diagnosis of ADHD. The researchers hypothesized that preschool children with ADHD would have higher levels of CU traits than TDC, and the researchers expected ICU to achieve incremental validity in the prediction of ADHD after adjusting for covariates. These three aims will help determine if the ICU could be specific and sensitive for ADHD diagnosis in preschool children.A total of 89 participants with ADHD and 87 TDC were included in this study. The clinical diagnosis interviews were conducted by psychiatrists based on the Diagnostic Infant Preschool Assessment (DIPA) , 4. In tThe current study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Xinhua Hospital affiliated with Shanghai Jiao Tong University . Parental written informed consent (only one parent needed to sign the written informed consent), child assent, and school agreement was obtained before children could participate in the study. All preschool participants’ parents and the school provided written informed consent and the children provided verbal assent. Similarly a written information sheet was provided to parents, the school, and verbal information using language that 4–5 year olds could understand, was provided to the children before consent and assent were obtained.The two groups, ADHD and TDC participated in the study from October 2016–May 2018 and there were different approaches to recruitment. With the ADHD group the participating preschool children and their parents were recruited from the outpatient clinic in the Department of Clinical Psychology, Xinhua Hospital affiliated with Shanghai Jiao Tong University. Children included in the ADHD group met the criteria for ADHD based on a clinical diagnosis with the DSM-5. The TDC group consisted of 87 healthy control children within the same age range. They were recruited from two kindergartens in the urban areas of Shanghai. The children in the TDC group were excluded if they met the diagnostic criteria for ADHD or other psychiatric disorders.Diagnostic Infant and Preschool Assessment (DIPA). The DIPA (version 2/28/14) was developed and updated by Scheeringa according to DSM-5 in 2014 [ in 2014 . The DIP in 2014 .Swanson, Nolan, and Pelham Rating Scale-IV (SNAP-IV) [SNAP-IV)  was usedSNAP-IV) .Inventory of Callous-Unemotional Traits (ICU) [ts (ICU)  includests (ICU)  that theBehavior Rating Scale of Executive Function-Preschool Version (BRIEF-P) [BRIEF-P)  parent fBRIEF-P) .Strengths and Difficulties Questionnaire (SDQ) [The parent-rated re (SDQ)  was usedre (SDQ) .The medical histories of the TDC children were briefly reported by their teachers and parents to exclude children with obvious medical and developmental problems. The children in the case groups and their parents were interviewed in the clinic, and primary diagnoses were made by psychiatrists according to a comprehensive medical history evaluation and psychiatric examination. Informed consent/assent was obtained before conducting any of the assessments. Parents completed questionnaires. The final diagnosis followed the criteria of the DSM-5 by DIPA.P > 0.05.The data was analysed using SPSS Statistics version 22  and EpiData3.1 . To characterize the sample, frequencies, correlations, means, and standard deviations (SD) were calculated for the variables of interest. Statistical differences in the variables were examined by Analysis of Variance (ANOVA) for continuous variables and Pearson’s chi-square tests for categorical measures. Pearson’s correlations were also used to examine the association between the CU traits, EBPs, and EF. Furthermore, multiple linear regression analysis was performed to assess the incremental validity of the ICU, adjusting for possible covariates by child’s sex, CPs, and oppositional defiant symptoms. Before that, possible covariates were selected based on the previous studies and a correlation analysis was conducted. Finally, the specificity and sensitivity analyses of the ICU was performed using a receiver operating characteristic (ROC) curve. Non-parametric estimates of the area under the curve (AUC) from the ROC curve analyses quantified the diagnostic efficiency of the ICU scale scores. All tests were two-tailed and F = 1.37, P = 0.26), sex , and IQ  among the three groups (P > 0.05).A total of 176 four- to five-year-old preschool children (89 with ADHD and 87 TDC) were enrolled in this study. The demographic and clinical characteristics of the ADHD group (including ADHD only and ADHD + ODD) and TDC group are presented in Appendix (P < 0.001) and they were statistically significant after controlling for the child’s age and sex. However, there were no significant differences between children with ADHD + ODD and children with ADHD only in the uncaring and unemotional scores.The comparison results of the ICU subscale scores among the ADHD only, ADHD + ODD, and TDC groups were analysed by one-way ANOVAs and post hoc tests, as presented in Table P = 0.02), CP  and peer problems , and negatively associated with prosocial behaviours . ICU was positively associated with EF performance .As seen in Table β = 0.55, R2 = 0.20, P < 0.001) than hyperactivity symptoms . The CU traits were also associated to oppositional defiant symptoms . The ICU score was significantly correlated with ADHD symptoms and explained a proportion (15–24%) of the variance, as shown in Table Linear regressions were performed to assess the association of CU traits and oppositional defiant and ADHD symptoms (inattention and hyperactive/ impulsive). In the linear regressions, the correlations were all significant, with inattention symptoms having higher beta values  and ADHD symptoms , as shown in Appendix Given the correlation between CU traits and CPs and oppositional defiant symptom, the relationship between ICU factors and ADHD symptoms may be affected by confounding factors such as CPs and oppositional defiant symptom. Taking age, sex, IQ, CPs, and oppositional defiant symptoms as independent variables, and CU traits and ADHD symptoms as dependent variables, the results of the single linear regression models showed that sex, CPs, and oppositional defiant symptoms were significantly related to CU traits , oppositional defiant symptoms , and CU traits  predicted the presence of ADHD symptoms. When the influence of sex and oppositional defiant symptoms were controlled, the analysis results showed that ICU could be used to explain 6% of the incremental validity in ADHD symptoms.Multiple linear regression analysis was performed to assess the incremental validity of the ICU, as shown in Table β) were calculated by converting all predictors and outcomes to z-scores before the analysisThe standardized regression coefficients .The ROC was calculated to analyse the specificity and sensitivity of the ICU scale in the diagnosis of ADHD, and to differentiate the ADHD group from the TDC group. According to the ROC curve, shown in Fig. The current study focuses on the importance of CU traits. Using the ICU, the results of our study showed that CU traits in Chinese preschool children with ADHD only and ADHD + ODD were significantly higher when compared to those children in the TDC group. This study is, to our knowledge, the first to examine the characteristics of CU in a clinical sample of Chinese preschool children who had ADHD comorbid with ODD. Furthermore, previous studies of children with ADHD have not measured the correlations between CU traits, EBPs, EF, and the effect of CU traits on the symptoms and diagnosis of ADHD. The incremental validity of the ICU was measured to investigate whether the uitility of the ICU was associated with increasing diagnostic efficacy of ADHD. Furthermore, identifying preschool children who potentially have heightened levels of CU traits is indicative of which children might be at risk of developing ODD or CD.The current findings are consistent with previous studies , which sForthemore, the research team found that compared with ADHD only, children with ADHD + ODD had higher callousness subscale scores. Detecting differences in CU traits in children with ADHD is particularly important to identify possible populations with co-occurring ODD. In this study the callousness traits of ADHD + ODD was similar to previous studies, demonstrating that elevated scores for CU characteristics increase the odds of ODD . AlthougThe results further revealed that higher levels of CU traits were related to lower levels of prosocial behaviours, such as less consideration of others, sharing with others, and providing help to people in difficulty. Meanwhile, children with high levels of CU traits had more difficulties with EBPs including CPs and peer problems. This finding supports previous evidence in dicating that CU traits are correlated with the severity of behavioural disturbances . More spThis research also confirmed that CU traits were marginally correlated with EF. These findings are supported by previous studies, which suggest that EF deficits in children with ADHD are conditional upon the level of CU traits , 29. StuIn terms of the influence of CU traits on ADHD assessment, preliminary analyses demonstrate that CU traits are significantly associated with ADHD symptoms, and it is one of the risk factors for the presence of ADHD. The influence of total score of ICU on ADHD symptoms can reach 15–24%. Moreover, the dimensional analyses indicated that elevated levels of CU traits may also emerge as a predictor for the risk of ADHD in young children when controlling for other possible covariates, such as child sex, CPs, and oppositional defiant symptoms. The incremental predictive utility of ICU showed that the CU traits could be used to explain 6% of the variance in ADHD symptoms. Although this research did reveal that the influence of CU traits was relatively weak as a risk factor for ADHD. Importantly, the researchers further calculated the specificity and sensitivity of the ICU scale in the diagnosis of ADHD, which indicated that the diagnostic value of ICU for ADHD was medium. Taken together, these findings suggest that CU traits are a proposed antecedent of ADHD and might be regarded as an auxiliary diagnosis tool of ADHD.Some limitations to the present study need to be addressed. Other cross-sectional and longitudinal studies with large samples of preschool children have suggested that when sex, temperament, executive function, and other covariates are controlled, preschool children’s CU traits are effective in predicting the occurrence of CD and ODD . HoweverThere is growing interest in investigating CU traits in ADHD children. The most important insight from this study is that the increase of ICU scores during early childhood tended to be associate with the likelihood of ADHD. These findings provide experimental evidence for the importance of taking ICU into account as an useful assessment tool for ADHD. Identification of early CU traits may help researchers better understand symptoms and behavioural problems in children with ADHD. Early detection may help to implement timely intervention strategies to prevent psychopathy and antisocial personality disorder developing.


Pteris vittata is an arsenic (As) hyperaccumulator plant that accumulates a large amount of As into fronds and rhizomes . However, the sequence of long-distance transport of As in this hyperaccumulator plant is unclear. In this study, we used a positron-emitting tracer imaging system (PETIS) for the first time to obtain noninvasive serial images of As behavior in living plants with positron-emitting 74As-labeled tracer. We found that As kept accumulating in rhizomes as in fronds of P. vittata, whereas As was retained in roots of a non-accumulator plant Arabidopsis thaliana. Autoradiograph results of As distribution in P. vittata showed that with low As exposure, As was predominantly accumulated in young fronds and the midrib and rachis of mature fronds. Under high As exposure, As accumulation shifted from young fronds to mature fronds, especially in the margin of pinna, which resulted in necrotic symptoms, turning the marginal color to gray and then brown. Our results indicated that the function of rhizomes in P. vittata was As accumulation and the regulation of As translocation to the mature fronds to protect the young fronds under high As exposure. Pteris vittata is the first fern to be identified as an As hyperaccumulator that can accumulate As to > 22,000 mg/kg dry weight in fronds when grown in soil spiked with As at 1500 mg/kg2. As concentration in contaminated water can also be reduced by P. vittata to a level that is within the World Health Organization (WHO) guidelines3. Different from other remediation methods, phytoremediation of As-contaminated soils and water by P. vittata is more environmentally friendly. P. vittata is a unique model to study the interactions between plant and As.Arsenic (As) is a toxic element that is usually found in soils and groundwater as a result of both natural and anthropogenic processes. Chronic exposure to inorganic As may lead to cancers of prostate, bladder and skinP. vittata, AsV is taken up by roots via phosphate transporters5, and then AsV is translocated from roots to rhizomes and reduced to AsIII7. Different from other plants, rhizomes are the unique underground stems that connect the roots and fronds in fern plants like P. vittata9. In many studies rhizomes and roots in P. vittata are not separated and are both recognized as roots11. Thus, there is little available information on As accumulation in rhizomes of P. vittata.Arsenate (AsV) and arsenite (AsIII) are two major inorganic As species in water, with AsV the more predominant form in aerobic water. In P. vittata12. In fronds, As accumulation and distribution have been well studied by several methods, including scanning electron microscope with energy dispersive X-ray spectrometer (SEM–EDX) and synchrotron X-ray microprobe analysis. These studies found that As was concentrated at the tip of the apical pinna and decreased toward the basal pinna of fronds13. Inside the pinna, As was found in the proximity of veins in the pinna surface, and most of the As was localized near the midrib, which indicated that As in the pinnae is contained in the apoplast rather than vacuoles14. Necrotic symptoms in the pinna margin were induced by high As exposure, and the As concentration in the pinna margin was 2.3 times higher than that in the midribs15. These techniques only provide static data and because of the detection limit, only high As exposure experiments were conducted. Therefore, As accumulation and distribution in fronds of P. vittata under various As conditions are still unclear.As (AsV and AsIII) in rhizomes is translocated to the fronds through the xylem in 16. PETIS is specially designed for studying plants and provides serial time-course images  of the two-dimensional distribution of a radiotracer within a living plant without contact; it has been applied to many studies on plant nutrition over the last decade17.The transport of metals in plants, including Fe18, Mn19, Zn21 and Cd23 have been visualized using PETIS. As transport has not yet been visualized in the plant by PETIS.The positron-emitting tracer imaging system (PETIS) is one of the most advanced radiotracer-based imaging methods available today; its principle is the same as that of positron emission tomography, which has been widely used for medical diagnosisP. vittata with high As exposure (30 mg/L) in a long term phytofiltration experiment. We successfully visualized As translocation and accumulation in living P. vittata from low to high As exposure by using PETIS and autoradiography with positron-emitting 74As labeled tracer and compared it with that in the non-accumulator model plant Arabidopsis thaliana. This study provides important insights into the behavior of As in plants as well as providing significant information to advance our understanding of As hyperaccumulation.In this study, we investigated As accumulation in rhizomes, and As accumulation in different stages of fronds in hydroponically grown P. vittata with high As exposure, P. vittata was grown hydroponically for 16 weeks in phosphate (Pi) free hydroponic medium with 30 mg/L AsV. Within 16 weeks more than 60% of the AsV was taken up by P. vittata (Fern) when compared with the negative control (NC) . After 16 weeks the rest of the plants were sampled and separated into fronds, rhizomes and roots. When compared with plants at 4 weeks, the As concentrations in the 16-week plants were comparable between fronds and rhizomes  and much higher than that in the roots . This result showed that rhizomes and fronds accumulated the high levels of As, which differed from the results of a previous study that showed that the only function of rhizomes was AsV reduction and AsIII translocation to fronds8. The fronds were then separated into three different types: fronds brown margin (tissue necrosis), fronds gray margin (tissue necrosis) and fronds new . Our results were supported by a previous study of high As concentration soil cultivation with P. vittata15, but our study showed a significant difference between fronds brown margin with fronds gray margin and fronds new, and much higher As concentrations in the fronds because the availability of As in hydroponic cultivation was higher than that in soil cultivation. On a dry weight basis, more than 95% of As was accumulated in fronds and rhizomes  were also tested.As translocation and accumulation in 74As-labeled AsV (0.0074 pmol) and subjected to PETIS for 7 days (168 h). Figure P. vittata. Time courses of the radioactivity of 74As over time within each region of interest (ROI) were generated through manual selection of ROI on the image data   Fig. a. Figure8 h Fig. b,c. In Aots Fig. d.Figure 74As. This provided the static distribution of As in individual fronds, rhizomes and roots for P. vittata . After 7 days of 0.2 and 20 μM As exposure, fronds did not show any necrotic symptoms but strong accumulation of 74As was observed in the fiddlehead and apical pinna of young fronds (brown arrow). Accumulation of As in the middle-to-basal pinna of young fronds and whole mature fronds were observed in the midrib (green arrow), rachis (yellow arrow) and sporangia (blue arrow)  Fig. c, which P. vittata exposed to 0.2, 20, 200 and 2000 μM non-radioactive AsV as well as 74As, respectively. When compared with roots (purple arrow), strong accumulation of 74As in rhizomes (red arrow) was observed. The pattern of As accumulation in rhizomes from 0.2 μM to 2000 μM was confirmed by the PETIS imaging at 168 h , lateral root (purple arrow) and shoots base (red arrow). The As accumulation in these tissues was higher than in the main-stem (yellow arrow) and shoots (green arrow) in A. thaliana. These results showed that a non-accumulator A. thaliana took up As from the solution through the roots and translocated As to shoots through a shoot base, but most of the As was retained in the roots.Figure 74As-labeled AsV. The As-hyperaccumulator P. vittata is efficient in AsV uptake, with the recent identification and investigation of a group of phosphate transporters in roots of P. vittata that were responsible for AsV uptake5. This is supported by the data of long term phytofiltration experiment and PETIS imaging presented here, which showed that AsV in solution was significantly decreased with different levels of AsV exposure  was translocated to fronds via the xylem in P. vittata8. However, our data revealed that rhizomes in P. vittata not only translocated As to fronds but also accumulated the same high level of As as the fronds  and regulating the As translocation to fronds. With low As exposure, most of the As is translocated and accumulated in young fronds. With high As exposure, As accumulation is shifted from young fronds to the mature fronds, especially in the margin of the pinna and induces the progression of necrotic symptoms from gray to brown.In this study, using a combination of a long term phytofiltration experiment and PETIS imaging and autoradiography analysis, we proposed the possible mechanism of As translocation and accumulation in Pteris vittata used in the present study was procured from Fujita Co., Ltd. . Arabidopsis thaliana ecotype Columbia (Col-0 accession) was kindly provided from Prof. Tomonobu Kusano .P. vittata seedling was conducted as described previously25. Three-month-old fern seedlings were individually transplanted into a hydroponic tank with surface area of 30 cm × 30 cm filled with 15 L of modified Hoagland nutrient solution (16 seedlings per tank). After four months of hydroponic cultivation, the 16-week As phytofiltration experiments including negative control (NC) were started  into 15 L Pi-free 1/5 modified Hoagland solution. Solution samples were collected at 0, 1, 2, 3, 6, and 16 weeks after the As exposure and the As concentration was measured. To investigate As accumulation in the fern, three ferns were collected (one plant from each tank) after 4 weeks of the exposure, and after 16 weeks of the phytofiltration experiment, the rest of the plants were harvested, and separated into fronds, rhizomes and roots.The AsV concentration was adjusted to 30 mg/L by adding sodium arsenate (Na1.As concentrations in the hydroponic solution and digested plant tissue samples were determined using an inductively coupled plasma mass spectrometer (ICP-MS)  as described previouslyThe values reported in both text and figures are the mean ± SE (standard error of the mean). The statistical significance (at 95% confidence) was tested using ANOVA.74As radioisotope (T1/2 = 17.7 d) was produced as previously desccribed26. Ga2O3 powder(0.1 g) was prepared as a 10 mm diameter pellet. It was covered with aluminum foil (10 μm thickness) and used as a target. The target was irradiated with an alpha beam  for 387 min from an AVF cyclotron at the Cyclotron and Radioisotope Center (CYRIC) in Tohoku University. The following processes for tracer preparation were started after the byproducts such as 71As (T1/2 = 65 h) and 72As (T1/2 = 26 h) decayed out at least 4 weeks after the irradiation. The irradiated Ga2O3 was dissolved in 1 mL of 12 M NaOH by heating up to 80 °C. After all the Ga2O3 was dissolved, about 2 mL of 6 M hydrochloric acid (HCl) was added to adjust the pH to ~ 6. The solution became a white suspension. The suspension was solvent extracted with 5 mL of 1 M Di-(2-ethylhexyl) phosphoric acid organic solution with hexane by shaking for 1 h. Ethanol was added to the aqueous phase after solvent extraction with hexane (about 3 mL) to make up the volume to about 50 mL, and sodium chloride (NaCl) in the solution was precipitated. For anion exchange column chromatography, 2 mL of Muromac 1 × 8  changed to OH– form was used. The supernatant was charged into this column. Ethanol and contaminants were washed off by flushing 20 mL of H2O on the column. The As tracer was eluted with 8 mL of 0.1 M HCl. The eluate was neutralized with 0.8 mL of 1 M KOH (pH ~ 7). After that, the AsIII/AsV ratio after the dissolution of the target in H2O was determined by thin layer chromatography27. Ten μL of the solution was spotted about 1 cm from the end of a Si-60 thin layer on the Al plate. The plate was developed for 10 min by a mixture of 0.01 M aqueous sodium hydrogen L-tartrate /methanol solution in a ratio of 3/1 as mobile phase. The locations of the origin, AsIII, and AsV on the developed plate were then identified by Electronic Autoradiography . The retention factors (Rf) of 0.6 and 0.9 were used to specify AsIII and AsV, respectively. Only AsV was detected  with nonradioactive AsV was fed to each test plant.21. All imaging experiments were conducted in a clean booth (2 m × 2 m × 2 m) under controlled temperature and humidity conditions with continuous light at a density of 70 μmol m−2 s−1; 25 °C.The PETIS imaging experiments were conducted following the methods of Fujimaki et al. and Suzui et al. with some modificationsP. vittata, the roots of intact P. vittata plants were inserted in an acrylic square container , and the fronds were sandwiched between 2 mm thick acrylic boards to maximize the annihilation of positrons. The acrylic board, which held four plants at a time, was placed in the field of view of the PETIS  . The behavior of 74As in the plants, including the roots, rhizomes and fronds, was monitored by the PETIS every 5 min for 168 h. The solution was continuously stirred with gentle aeration to maintain a uniform composition in each compartment of the container.For P. vittata, the PETIS imaging of A. thaliana was conducted with 0.2 μM AsV. The experimental conditions and preparation were the same as the PETIS imaging of P. vittata.Based on the PETIS imaging results of 74As at a certain time before plant exposure. Therefore, the amount of total As (i.e. sum of radioactive and nonradioactive As) corresponding to the radioactivity of 74As at a given time can be easily determined. All the graphs and PETIS images shown in this paper indicated the determined amounts of total As, not just the intensities of the 74As signal. The time course data of the As amount (mg) in the regions of interest in the images were calculated by the values of the signal intensity (cps) extracted using the NIH Image J 1.50 software (http://rsb.info.nih.gov/ij/), counting efficiency of the system (cps Bq−1) and specific radioactivity (Bq mol−1).In the PETIS imaging experiments, the indicated amounts of nonradioactive As were mixed with measured activities of pure 74As in plant.The test plant was dissected and placed on paper sheets after the PETIS imaging was finished and then placed in contact with imaging plates  in cassettes. After 2 days of exposure, the imaging plates were scanned using a bio-imaging analyzer  to obtain the auto-radiographic images of Supplementary Figures.Supplementary Video 1.Supplementary Video 2.


While observational studies revealed an inverse association between serum 25(OH)vitamin D (25(OH)D) and the risk of attention deficit/hyperactivity disorder (ADHD), the causality of this relationship remains unclear.We conducted a bidirectional two-sample Mendelian Randomization (MR) study to examine whether 25(OH)D has an effect on the risk to develop ADHD or vice versa. Information on single nucleotide polymorphisms (SNP) associated with serum 25(OH)D was obtained from a genome-wide association study (GWAS) considering phenotype data from 79,366 individuals of European ancestry. Data on risk for ADHD were derived from a GWAS analysis with 20,183 individuals diagnosed with ADHD and 35,191 controls. For our analysis, we considered effect sizes based on the European participants .p = 0.024). The overall MR estimates did not reveal a causal effect of 25(OH)D on risk for ADHD. In the reverse analysis, neither any single nor the multi-SNP MR analyses showed a causal effect of ADHD on 25(OH)D.Single SNP analyses showed a causal effect of vitamin D on ADHD risk for only one SNP D on ADHD or vice versa. Accordingly, our study does not provide evidence that improving 25(OH)D via supplementation could reduce the risk of developing ADHD.The online version of this article (10.1007/s00394-020-02439-2) contains supplementary material, which is available to authorized users. Considering its impact on calcium and phosphate metabolism, vitamin D had initially been discussed in particular in the context of bone health . While EIn a systematic literature search regarding childhood and adolescence, we recently identified a large number of  studies dealing with the relationship between vitamin D and mental health . BesidesIn general, vitamin D effects on mental health seem plausible considering the fact that both vitamin D receptors and metabolizing enzymes are located in the brain . With reA meta-analysis recently summarized results from four RCTs with vitamin D supplementation in ADHD patients and revealed a small, but significant improvement of ADHD symptoms . HoweverIn contrast, Mendelian randomization (MR) studies based on summary data from GWAS are a time-effective approach using genetic variants as instrumental variables (IVs) to examine the causal effect of blood concentrations of a specific nutrient (e.g. vitamin D) on a particular disorder (e.g. ADHD) . The conGenetic factors play an important role for levels of circulating vitamin D  with herWe conducted a two-sample MR analysis with 25(OH)D as exposure and ADHD as outcome variable and a reverse analysis with ADHD as exposure and level of 25(OH)D as outcome. Information on single nucleotide polymorphisms (SNPs) associated with serum 25(OH)D were obtained from the latest GWAS analysis at the time of our data analysis considering phenotype data from 79,366 individuals of European ancestry including 31 studies from epidemiological cohorts from Europe, Canada, and USA . We usedData on risk for ADHD were derived from a GWAS analysis with 20,183 individuals diagnosed with ADHD and 35,191 controls from 12 cohorts [2 ≥ 0.40, on the basis of GRCh37.p13, Ensembl version 87, 1000 genomes: phase 3 version 5 for European ancestry, were exported applying the in silico tool SNiPA [http://www.snipa.org. Accessed on 19 August 2019). Post-hoc selection criteria for proxy-SNPs were defined: 1st highest r2, 2nd smallest distance to the lead SNP. In the sensitivity analysis for reverse MR, we selected the proxy SNPs with the following selection criteria: 1st highest r2, 2nd smallest distance to the lead SNP, 3rd no palindromic alleles. Because of the potential problems with the strand [In case of unavailability of SNPs identified in the 25(OH)D GWAS in the ADHD GWAS or vice versa, we used proxy-SNPs as recommended . For theol SNiPA  D concentrations revealed substantial larger numbers of genome-wide significant loci. Revez and colleagues used data from 417,580 European participants from UK Biobank and observed 143 independent loci . ManousaFor these two sensitivity analyses, we defined an alternative genetic instrument based on results from the respective GWAS on 25(OH)D and conducted separate MR analyses. Only those SNPs from the respective GWAS on 25(OH)D were considered which were both available in the ADHD GWAS and not ambiguous or palindromic. This requirement was met by 100 from 143 genome-wide significant SNPs identified by Revez et al. and 61 from 138 SNPs identified by Manousaki and colleagues.As recently suggested  we used p < 5 × 10–8). For reverse analysis with ADHD as exposure, we used SNPs which were genome-wide significantly associated with ADHD (p < 5 × 10–8) with the risk of ADHD as the genetic instrument. (2) There is no association between the genetic variants and confounders of the relationship between risk factor and outcome. (3) Conditioning on the risk factor and possible confounders, there is no direct association between the genetic variants and outcome [To conduct a MR study, three core assumptions must be fulfilled : (1) The outcome . Thus, t outcome .From these assumptions, only the first assumption can be directly tested . For thiForest and scatter plots were used to visualize combined results of single and multi-SNP analyses. The scatter plots show the single SNP effects on the exposure against the single SNP effects on the outcome with corresponding standard deviations and estimated regression lines of the multi-SNP analyses. We performed a power analysis to estimate whether our analysis, given sample size, proportion of cases in the study, and the proportion of variance explained, provides sufficient statistical power to detect a true causal effect .https://github.com/MRCIEU/TwoSampleMR) and MR-PRESSO (https://github.com/rondolab/MR-PRESSO).All tests were performed using the software “R “, version 3.5.2, and R-packages for performing 2-sample MR (p < 5 × 10–8 with 25(OH)D are presented in Table We performed a MR analysis to investigate the causal effect of 25(OH)D levels on risk of ADHD. The data on the six SNPs associated with the genome-wide significance of F = 1558.762, the F-statistic indicates strong instrumental variables (variance explained by the six SNPs (2.84%) was used for the calculation). There was no evidence for pleiotropy  or heterogeneity (Cochran's Q (df = 3) = 4.594, p = 0.204). MR-PRESSO also revealed no evidence for horizontal pleiotropy. Single SNP MR analyses showed that only SNP rs12785878 (NADSYN1-DHCR7) was associated with ADHD. The overall estimates calculated by IVW or MR-Egger, did not reveal an overall causal effect of 25(OH)D levels on the risk of ADHD . However, the leave-one-out analysis showed that the exclusion of SNP rs3755967  would lWe conducted additional sensitivity MR analyses using alternative genetic instruments considering results from two recent GWAS on 25(OH)D , 24. BotF = 21,867.14, the F-statistic indicates strong instrumental variables  was considered as determined using those cohorts of European ancestry and all 12 genome-wide significant SNPs in the ADHD GWAS). There was no evidence for pleiotropy . Heterogeneity tests were not significant (Cochran's Q (df = 4) = 4.473, p = 0.346) and MR-PRESSO also revealed no significant outliers.To test reverse causality, we performed a MR analysis where we considered the risk of ADHD as exposure and 25(OH)D as the outcome. Data on the association of the 12 selected SNPs with ADHD and with 25(OH)D is presented in Table β = − 0.002, p = 0.887; MR Egger method: β = − 0.048, p = 0.377)  also revealed no significant overall effect. Leave-one-out analysis also showed no effect . Additionally, we performed a sensitivity analysis with proxy SNPs, which were neither palindromic nor ambiguous (Table S3). This analysis also showed no causal effect of ADHD on 25(OH)D .No single SNP showed a causal effect of ADHD on 25(OH)D. Further, the overall effect was not significant D levels on the risk of ADHD. Additionally, our analysis did also not indicate a causal relationship in the opposite direction, i.e. ADHD did not have an effect on 25(OH)D levels.p = 0.024). Also, one of the leave-one-out sensitivity analyses indicated a significant overall effect in the same direction, when rs3755967, indicating an inverse association, was excluded from the analysis. However, it has to be kept in mind that rs12785878 would not survive correction for multiple testing (five SNPs). Additionally, underlying pathophysiologic mechanisms of a harmful effect of 25(OH)D on ADHD are difficult to understand. Thus, this surprising finding underlines the fact that additional GWAS on 25(OH)D and ADHD are still urgently needed to increase the number of genome-wide significant SNPs and to further improve the genetic instrument for future MR studies.Interestingly, one of the five SNPs used for the definition of the IV (rs12785878) even indicated a significant positive effect of 25(OH)D on the risk of ADHD, i.e. higher risk for ADHD at higher 25(OH)D levels D and depression: Here, observational studies also showed positive associations , while tn = 79,366) [n = 20,183 cases) [To the best of our knowledge, our study is the first two-sample MR study on causal relationships between 25(OH)D levels and ADHD. A strength of our study is the consideration of recent, large-scaled GWAS on both vitamin D metabolism   and on A3 cases) , 24. SenA limitation of our MR study is that only six SNPs, which explain 2.8% of the variance of 25(OH)D levels, could be considered for the definition of the IV. One of these SNPs was ambiguous and palindromic and had thus had to be excluded. Thus, our instrument might be regarded as weak and bias the association with ADHD. However, the F-statistics indicated that our genetic instrument was sufficiently strong. Furthermore, our sensitivity analyses using a higher number of SNPs as genetic instrument confirmed results from our main analysis.Another general issue of MR studies is horizontal pleiotropy, i.e. an association between the MR instrument and the outcome of interest via pathways other than the suggested exposure . Even thWhen interpreting the results, it is important to note that the vitamin D GWAS did not focus on individuals with low (or high) levels of 25(OH)D. Since our MR analysis assumed a linear relationship between exposure and outcome, the results might not apply to people with a severe 25(OH)D deficiency, but to the general population . AdditioSupplementary file1 (DOCX 10573 KB)Supplementary file2 (DOCX 33 KB)Below is the link to the electronic supplementary material.


Background: The diagnosis of attention deficit hyperactivity disorder (ADHD) relies on history and observation, as no reliable biomarkers have been identified. In this study, we compared a large single diagnosis group of patients with ADHD  to healthy controls using brain perfusion single-photon emission computed tomography (SPECT) imaging to determine specific brain regions which could serve as potential biomarkers to reliably distinguish ADHD.Methods: In a retrospective analysis, subjects  were obtained from a large multisite psychiatric database, where resting state (baseline) and on-task SPECT scans were obtained. Only baseline scans were analyzed in the present study. Subjects were separated into two groups – Group 1  was composed of patients who only met criteria for ADHD with no comorbid diagnoses, while a control group (n = 129) composed of individuals who did not meet criteria for any psychiatric diagnosis, brain injury, or substance use served as a non-matched control. SPECT regions of interests (ROIs) and visual readings were analyzed using binary logistic regression. Predicted probabilities from this analysis were inputted into a Receiver Operating Characteristic analysis to identify sensitivity, specificity, and accuracy.Results: The baseline ROIs and visual readings show significant separations from healthy controls. Sensitivity of the visual reads was 100% while specificity was >97%. The sensitivity and specificity of the post-hoc ROI analysis were both 100%. Decreased perfusion was primarily seen in the orbitofrontal cortices, anterior cingulate gyri, areas of the prefrontal cortices, basal ganglia, and temporal lobes. In addition, ROI analysis revealed some unexpected areas with predictive value in distinguishing ADHD, such as cerebellar subregions and portions of the temporal lobes.Conclusions: Brain perfusion SPECT distinguishes adult ADHD patients without comorbidities from healthy controls. Areas which were highly significantly different from control and thus may serve as biomarkers in baseline SPECT scans included: medial anterior prefrontal cortex, left anterior temporal lobe, and right insular cortex. Future studies of these potential biomarkers in ADHD patients with comorbidities are warranted. Attention deficit hyperactivity disorder (ADHD) is one the most costly psychiatric disorders, conservatively estimated to be around 42.5 billion USD annually . ADHD isFurthermore, the cardinal symptom of ADHD—inattention—is a non-specific symptom. Inattention is found not only in ADHD, mania, anxiety, and depression, but it is also found in traumatic brain injury, carbon monoxide poisoning, cadmium toxicity, lead toxicity, schizophrenia, post-traumatic stress disorder (PTSD), post-coronary bypass syndrome, multiple sclerosis, substance abuse, space-occupying lesions, CNS infections, dementia, and a litany of other conditions which alter frontal lobe functioning. Distinguishing among these alternatives by interview alone is challenging, because, for instance, there is no specific question that will reveal lead toxicity or cadmium toxicity. Similarly, an interview may or may not uncover a history of brain injury, depending on the degree of anterograde amnesia or how the patient has trivialized the impact of a concussive event. The authors have seen numerous cases of drowning, toxicity, post-pediatric surgery, hypomania, and irritable depression which were misdiagnosed clinically as ADHD. An additional challenge in diagnosing ADHD, regardless of age, is that comorbidity is the rule, rather than the exception in ADHD.The base level of precision in the diagnostic evaluation of ADHD is the use of rating scales. Measures such as the Vanderbilt Rating Scale , and theA higher level of accuracy can be derived by the use of computerized tests of attention. While there is perception that continuous performance tests are the “objective standard” for ADHD diagnosis, the research demonstrates a distinct gap between the computerized diagnosis and the clinical presentation. For example, correlation between the Conners Continuous Performance Test (CPT) results and results of parent or teacher symptom rating scales is low to moderate , 12. TheThe Food and Drug Administration's (FDA) Biomarkers, EndpointS and other Tools (BEST) glossary defines a biomarker as: “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions” . Note thQuantitative electroencephalogram (qEEG) has been FDA-approved as a diagnostic tool for ADHD and purported to serve as a biomarker. However, the marker of elevated theta/beta wave ratio is not reliably diagnostic. The pivotal study on the ratio reported a 20% false negative rate . MoreoveAnatomical magnetic resonance imaging (MRI) studies have found a small number of consistent findings across the age-range of ADHD . MultiplOne limitation of this anatomical MRI research is that a majority of the studies do not control for comorbidities or medication use. Also, the relative paucity of longitudinal studies precludes determining if the brain volume changes represent a persisting difference or a delay in maturation. An additional limitation of this work is the high degree of variability within groups for any given metric . Lastly,ADHD has been the subject of intense study using functional MRI over the past 24 years, yet the results have been highly divergent –32. MultThe overall findings indicate a lack of regional convergence in children/adolescents with ADHD, which might be due to heterogenous clinical populations, various experimental design, preprocessing, (or) statistical procedures in individual publications.”  failed to show increased perfusion in the anterior cingulate bilaterally and in the left orbitofrontal prefrontal cortex during an inhibitory task  compared to 15 age-matched controls (age 13.2 ± 1.9). These authors further analyzed the ADHD subjects by comparing children who were medication-naïve and those who were not. These two subgroups did not differ in performance or functional neuroimaging findings  or using selected kernels to identify networks of activation, have suggested that ADHD is not a disorder of isolated brain regions, but more of a connectivity disorder . NeverthIn addition, multiple areas of the cerebral cortex, including parietal and temporal regions, as well as the cerebellum, have shown decreased activity during concentration tasks in subjects with ADHD . Thus, iMachine learning or artificial intelligence (AI) techniques have been applied to neuroimaging in an effort to detect patterns and findings not evident from simple statistical analysis. Numerous AI techniques, such as support vector machine, multiple kernel learning, deep belief network, convolutional neural network and others have been applied to fMRI and anatomical MRI data . For exaFunctional neuroimaging studies of ADHD utilizing single-photon emission computed tomography (SPECT) in children and adults have included baseline studies, cognitive challenge studies, and medication effect studies. The controlled clinical trials have been small with a range of 6–54 subjects (reviewed in Discussion). Our earliest study in this area included 54 children who met DSM-III-R criteria for ADHD compared to a clinical group of 18 children who did not meet those criteria . Visual,Findings across multiple studies are consistent but are they reliable and can they be used on an individual basis to predict the diagnosis of ADHD? This question is central to the ability to use a SPECT neuroimaging finding as a biomarker. Herein, we describe the first step in an analysis of a community dataset totaling over 100,000 patients. We describe the analysis of adults with ADHD free of comorbidity compared to a control group who lack any psychiatric or neurological diagnoses. Future steps will include comparisons of comorbid ADHD across age groups and predictive modeling utilizing machine learning algorithms involving iterative comparisons of data subsets to assess the predictive value of specific biomarker candidates.http://www.integreview.com/). In this retrospective analysis, all study subjects were patients at Amen Clinics, Incorporated (ACI), a multidisciplinary group of psychiatric clinics that incorporates SPECT neuroimaging into diagnostic assessment and treatment  which included persons that met the DSM-IV criteria for ADHD (n = 129). The Control group was recruited using local advertisements in newspapers and local colleges. Each subject met the clinical criteria for a healthy brain subject based on our criteria that included the absence of current medical illnesses, brain trauma, family history of psychiatric illness, drug/alcohol abuse and no current or past evidence of behavioral or psychiatric issues as measured by a detailed clinical history, Minnesota Multiphasic Personality Inventory (MMPI) and Structured Clinical Interview for Diagnosis (SCID) for DSM-IV. The Control group recruitment and scanning study protocol was approved by Western IRB (WIRB # 20021714). All subjects were fully informed and gave their written consent.This study adhered to the STAR-D guidelines  (see Supreatment . Methodsfor ADHD  and no ofor ADHD . The diafor ADHD . The ADHBrain SPECT was applied as previously described in published work using standard methods . To reviAll images were processed using Odyssey software (Picker), with transaxial slices oriented horizontal to the AC-PC line. Coronal, sagittal, and transaxial slice images  were then rendered in the Odyssey step-20 scale, a commercial scale included in the Odyssey software package, which scales all voxels to the brain maximum and assigns each a color gradient based on its percentile of activity. Each color step represents a (not necessarily linear) five-percentile-point change in rCBF.Baseline images were acquired in the following manner, adapted from the Society of Nuclear Medicine and Molecular Imaging Procedure Guideline for Brain Perfusion SPECT . PatientMethods for clinical interpretation of SPECT scans by visual read have not changed during the 17 years of patient evaluations from which these data are drawn. These methods have already been fully described in prior peer reviewed work . To revipost-hoc ROI analysis. ROI counts were derived from the anatomical regions in the AAL atlas  . In Groun = 129) with a mean age of 45.4 ± 16.9 (p ≤ 0.001). Group 1 was 34% female and 31% non-Caucasian, while the control group was 44% female and 33% non-Caucasian (p < 0.001 in age group and non-significant for non-Caucasian). Group 1 did not meet criteria for any other psychiatric disorder, substance abuse, or brain injury based on a detailed clinical history, the Minnesota Multiphasic Personality Inventory (MMPI) and the Structured Clinical Interview for Diagnosis (SCID) for DSM-IV.The total sample of 1,135 subjects were separated into two groups. Group 1 consisted of patients who met the DSM-IV criteria for ADHD, and it contained 1,006 subjects see . The meapost-hoc ROI analysis of the Baseline scan was 100%. Sensitivity of the visual reads was 100% while specificity was >97%. The sensitivity and specificity of the post-hoc ROI analysis were both 100%. p < 0.001) for discriminating ADHD from control included: medial anterior prefrontal cortex, left anterior temporal lobe, and right insular cortex. Areas with moderately significant Odds Ratio (p < 0.03) included: right lateral middle temporal lobe, right medial temporal lobe, dorsal anterior cingulate gyrus, the genu of the anterior cingulate gyrus, and the left parietal cortex  calculations. The AUC for Visually Read ROI's of the Baseline scan was 97.6%. The AUC for the rtex see .post-hoc quantified ROI regions that were most predictive in distinguishing cases of ADHD in Group 1 from controls. In particular, cerebellar subregions were very predictive (p < 0.001), along with the bilateral anterior inferior temporal lobe, bilateral middle temporal pole, and the bilateral inferior occipital lobe. Areas with significant predictive findings (p < 0.03) included: bilateral anterior cingulate gyri, bilateral mid orbital frontal cortices, right superior orbital frontal cortex, bilateral hippocampi, right middle occipital cortex, bilateral thalamus, and bilateral pallidum. Some left/right differences were observed. The left anterior cingulate showed a higher odds ratio than the right, while the right orbital frontal cortical areas had higher odds ratios than the corresponding areas on the left.post-hoc ROIs were not used in any way to initially establish the clinical diagnoses, they serve as a particularly rigorous independent predictor of diagnostic category. However, it is emphasized that nuclear medicine physicians and radiologists typically use visual analysis for readings SPECT scans in a clinical setting; thus the high sensitivity and specificity of the identified areas serve as promising steps for the translation of SPECT markers for ADHD more widely into clinical practice.In the adult population, there was high separation between non-comorbid ADHD patients vs. healthy controls using this method. The ROC characteristics were similar for the baseline scan data whether using visual or quantitative analysis. Since the biomarker as defined by the FDA BEST criteria  in the frontal cortices and often the temporal lobes and cerebellum , 61–66 tKim et al.  conducteLorberboym et al.  examinedRecently, these neuroimaging findings have been replicated using a newer technique of infrared spectroscopy . A samplUnfortunately, comorbidity is the rule, rather than the exception, in cases of ADHD. Therefore, a study of pure ADHD is not sufficient to identify a clinically useful imaging biomarker for ADHD. Children with ADHD frequently have comorbid anxiety, oppositional disorders, or learning disorders , 74. ForADHD in adults was largely unrecognized prior to 2002 , 83. ComHerein, we have analyzed baseline SPECT scans of adult patients with pure ADHD compared to an age-matched control group. Interesting potential candidate neuroimaging biomarkers have been identified. Next, it will be vital to expand these analyses to patients with ADHD comorbid for diagnoses such as anxiety, depression, bipolar disorder, addictions, gender-based differences, and/or brain trauma (among others). Going forward, we will also implement machine learning algorithms to rigorously test the candidate biomarkers utilizing our large-N dataset. These algorithms will be applied to our adolescent datasets to explicitly address the complex diagnostic picture of ADHD symptomatology in the adolescent patient.post-hoc ROI analysis which had similar findings. That said, several caveats of the study must be addressed. First, the data are retrospective, and higher levels of evidence can be derived from either prospective studies or randomized clinical trials. However, the large sample size and diverse multi-site study optimizes the generalizability of our results. Second, this dataset does not have associated structural imaging data. Such information would have been useful in characterizing any atrophy associated hypoperfusion. However, the use of functional neuroimaging is essential in characterizing subtle abnormalities that may not be apparent on even quantitative structural neuroimaging. Third, in such a large retrospective database, there are many patients who were on one or more medications for medical and psychiatric indications. While known stimulants and depressants of brain function were withheld prior to scanning, the confounding effects of medical and psychiatric medications cannot be completely eliminated.Strengths of this study include the large sample size of non-comorbid ADHD compared to a well-characterized control group, the consistent methods of visual interpretation of rest (or baseline) scans with a well-validated functional imaging modality, and detailed quantitative analysis. The study is further enhanced by a Using the method described above we consistently distinguished adult ADHD patients from healthy controls. Given the wide availability of brain SPECT imaging and the need for accurate diagnosis in ADHD, this test, with appropriate reader training, could provide valuable information in clinical practice.The original contributions presented in the study are included in the article/http://www.integreview.com/) and Western IRB (WIRB # 20021714). The patients/participants provided their written informed consent to participate in this study.The studies involving human participants were reviewed and approved by IntegReview . Since 2017, he has served in the SNMMI Brain Imaging Outreach Working Group. Currently, he serves as president of the International Society of Applied Neuroimaging. TH has no ownership in, and receives no remuneration from, any neuroimaging company. No more than 5% of his income is derived from neuroimaging. DA is the sole owner of Amen Clinics, a group of nine neuropsychiatric clinics that perform brain SPECT imaging. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.