A research technician describes how receiving an ADHD diagnosis allowed her to re-examine how she sees herself and her work. You need to pay more attention. If you’d rather watch the birds than follow the lesson, then get out of my classroom! You’ve got potential, but you aren’t trying hard enough to reach it. Why can’t you be like other children?…Why are you this way?The relentless chorus grew louder in my head, eventually drowning out all other sounds. I felt trapped by the voices, by the grip of the chair, by a tsunami of pain suddenly crashing onto my chest. In a lecture hall of 300 undergraduate students, nobody seemed to notice I was gasping for air.I had received so many of these comments growing up. When I relocated from Vietnam to the United States to study cell biology, I thought I might leave them behind. But no matter what I did, the voices kept plaguing my mind.I had always felt deep down that something was amiss about me – after all, no one else seemed to require exhausting amounts of mental effort to pay attention in class or to navigate mundane tasks – but I had never been able to understand why this was the case. In a way, it was easier to just comply with the deceivingly simple mantra drilled into me since childhood: “You just need to try harder”.Once, in a moment of desperation during my freshman year at university, I turned to Google. Amongst the long list of possible answers that ranged from sleep deprivation to brain tumors, I stumbled upon attention-deficit/hyperactivity disorder – ADHD. I went through the list of symptoms, ticking them off one after the other. Hope flickered as some semblance of clarity unfolded. Still, most articles painted a profile that felt too reductive, describing restless children uninterested in school and destined to fail in academia. I knew this wasn’t me. I enjoyed my science classes, my grades were good overall and I wasn’t the restless ball of energy people labeled as hyperactive. Maybe I didn’t have ADHD after all. Maybe I was merely moving at a slower pace, maybe I just needed to try harder – there it was again, the familiar whisper of self-doubt. The flicker of hope quickly faded away, leaving me to grapple with uncertainty once more.My suspicions resurfaced in my last month as an undergraduate when one of my friends and fellow classmates opened up about her diagnosis. Her struggles resonated with mine, yet she didn’t seem to fit the stereotypical portrayal of ADHD either. She encouraged me to get assessed, showing unfailing belief in my potential. The weight of facing my challenges alone lifted and with her by my side, hope ignited again. This time it was here to stay.I looked for an affordable ADHD assessment as soon as I got a job as a research technician after graduating. Shifting through a seemingly endless list of options, I finally managed to find the only provider in my area who was covered by my work health insurance and therefore wouldn’t cost me thousands of dollars.“Your symptoms and test results are consistent with ADHD”’.I felt relief, exhilarating relief. Finally, it all made sense. The hurricane of thoughts hurling through my mind from the moment I wake up, stopping me from focusing on the papers I want to read. The dispersed attention that unnecessarily latched onto random bits of a presentation. The unreasonable rage at the noise of the PCR machine, the beep of the lab door code, the squeaking chairs at seminars, the constant tickle of the hair poking my skin. The steps missed when following written protocols. The time blindness. The paralytic agony when a criticism feels like rejection. The unproductive workaholism.The fraud I thought I was.The diagnosis brought the clarity that had been missing my whole life, yet I couldn’t help but also feel betrayed. I thought about my childhood, growing up poor in a country where awareness of any neurodevelopmental conditions is a luxury. There is no translation for the word ‘neurodiversity’ in Vietnamese or even an equivalent. Wellbeing, mental health, or neurodivergence are shameful taboos that parents usually dismiss, ironically, by saying it’s all “in your head”. The cure to inattentiveness, forgetfulness, or impulsivity, in their opinion? Pay more attention. Remember better. Control yourself.I’d tried so hard but these simple “solutions” never worked for me, as if an invisible wall stood there in front of me. At home and school, adults attempted to ‘correct’ what I know now were my ADHD traits by belittling me, punishing me, or beating me. Little by little, I internalized these criticisms. By the time I left for the US, I believed that I was lazy, incompetent, and disobedient. Gullibly, I hoped that such self-condemnation would push me to grow and make me a better scientist. Instead, each day became an uphill battle. It took me too long to realize I was drowning in guilt and shame. The diagnosis finally opened my eyes: I had spent so much time blaming myself for not being able to fix what I didn’t know was there to be fixed.It took a comment from a postdoc in my lab for me to see what this could mean. Around the time he told me that eLife was interested in hearing from neurodivergent scientists, he encouraged me to think about how I would want my ADHD to be pictured. I understood then that I had the freedom to reshape my story. I could decide to change the internal lens through which I viewed my worth and start being kinder to myself. In that moment, I snapped back from an eternity of crisis. For the first time, I recognized that my ADHD traits could also be assets, the building blocks that make me unique as a scientist. My ability to hyperfocus has allowed me to become fully immersed in refining my technical skills or analyzing data. My thoughts may often get side-tracked, but this has also led to creative questions and new observations.In a way, this fresh perspective brought challenges of its own. To say that all the burdens of ADHD can be harnessed for good is wishful thinking. I believed I had to create a narrative that would allow me to acknowledge my ADHD without feeling defective, to forgive it while taking accountability for my mistakes, and to recognize its strengths while not romanticizing it as a superpower. I saw self-acceptance as a goal, something I could only reach after discovering this perfect balance and deciding on a final, positive stance about myself and ADHD. As I struggled to reconcile all these thoughts, I thought perhaps external insights could help.I mustered the courage to share the news of my diagnosis at work. I was blessed to receive the support of my lab members and my PI, who at times believed in me more than I believed in myself. However, not everyone in academia has been as supportive. I’ve received comments about how ADHD isn’t real or that I couldn’t possibly have received a diagnosis given how ‘high-functioning’ I was. I was warned that I had fallen victim to a narrative that “medicalizes normal behaviors”, while also being told I was taking “the easy way out” for seeking medication to manage my ADHD. These dismissive remarks stripped me of my true identity, leaving me feeling vulnerable and invisible. They confirmed my fears that people would think I was using ADHD as a feeble excuse for falling short; they fed into my doubts about the validity of my diagnosis, and whether I could succeed as a scientist in this environment.I know I share these experiences with many other neurodivergent friends and researchers. There’s an urgent need for safe spaces that offer neurodivergent academics validation and solidarity so we can thrive and contribute our unique perspectives to science. It’s disheartening to see that need ignored so often. Nevertheless, I find solace in knowing that I am not alone in coping with ADHD and in challenging the misconceptions around it. Through openness and advocacy, I am carving a path of acceptance and understanding, not only for myself but also for others who may share similar struggles.Ultimately, I’ve realized that I don’t need to find a perfect balance for myself. I certainly don’t owe it to anyone to create an ADHD narrative that accommodates everybody’s opinions. I will probably always oscillate between different emotions about my diagnosis; sometimes I may think that I’m bound to struggle forever, and sometimes I may feel empowered by it. And that’s OK. This is what self-acceptance is for me now. It’s not about having a rosy outlook on myself or glossing over the unpleasant emotions arising from my doubts and self-awareness. Instead, it’s embracing and welcoming it all, the good and the bad, the certain and the uncertain, the moments of strength and vulnerability alike.series of articles on being neurodivergent in academia, which includes a list of tips, resources and tools collated by neurodivergent scientists.This Sparks of Change article is part of a


Numerous voxel-based resting-state functional magnetic resonance imaging (rs-fMRI) measurements have been used to characterize spontaneous brain activity in attention deficit hyperactivity disorder (ADHD). However, the practical distinctions and commonalities among these intrinsic brain activity measures remain to be fully explored, and whether the functional concordance is related to frequency is still unknown. The study included 25 ADHD, combined type (ADHD-C); 26 ADHD, inattentive type (ADHD-I); and 28 typically developing (TD) children. We calculated the voxel-wise  and volume-wise  concordance among dynamic rs-fMRI indices in the slow-5 (0.01–0.027 Hz) and slow-4 (0.027–0.073 Hz) frequency bands, respectively. The spatiotemporal concordance within the slow-4 and slow-5 bands among the ADHD-C, ADHD-I, and TD groups was compared. Although the ADHD-C and ADHD-I groups showed similar volume-wise concordance, comparison analysis revealed that compared with ADHD-C patients, ADHD-I patients exhibited decreased voxel-wise concordance in the right median cingulate and paracingulate gyrus (MCC) and right supplementary motor area (SMA) in the slow-5 band. In addition, the voxel-wise concordance was negatively correlated with the diagnostic scores of ADHD subtypes. Our results suggest that functional concordance is frequency dependent, and dynamic concordance analysis based on specific frequency bands may provide a novel approach for investigating the pathophysiological differences among ADHD subtypes. The disorder can seriously affect patients’ social interactions in terms of difficulty concentrating and poor self-control. ADHD is characterized by strong heterogeneity and familial aggregation . The totResting-state functional magnetic resonance imaging (rs-fMRI) is an MRI analysis that measures the blood oxygen level dependent (BOLD) signals of patients in the absence of external stimulation . It is cAlthough there are definitional differences among these intrinsic brain activity indices, the practical distinctions and commonalities among these fMRI measures still need to be fully explored in the absence of a comprehensive neurophysiological perspective on the underlying mechanisms of ADHD. Yan et al. proposed a novel concept, dynamic concordance, to comprehensively explore the interdependent relationship among rs-fMRI indices in terms of temporal and spatial dynamics . While tMost studies on neural oscillations in ADHD patients have examined the conventional low frequency band (0.01–0.08 Hz). However, when Buzsaki et al. subdivided the full frequency of oscillations into subbands, they found that oscillations within different bands were related to various neurophysiological activities . PreviouOur study compared the spatiotemporal concordance within the slow-4 and slow-5 bands among the ADHD-C, ADHD-I and typically developing (TD) groups. The frequency and group interaction effect was analyzed to identify the temporal neurophysiological patterns of ADHD subtypes in frequency subbands. Based on previous research findings, we hypothesized that there would be differences in the dynamic functional concordance of intrinsic brain activity among ADHD subtypes. Our study may enable better understanding of the neurophysiological dysfunction patterns in ADHD subtypes.2.2.1.The data used in this research were obtained from the Peking University dataset in the ADHD-200 Consortium . The inchttp://fcon_1000.projects.nitrc.org/indi/adhd200/.The functional and anatomical MRI data were obtained with 3 T Siemens Trio scanners. The scan parameters were 2,000 ms for the repetition time (TR) and 30 ms for the echo time (TE). More detailed scanning parameters can be found in 2.2.http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit V1.8 (REST 1.8) (http://www.restfmri.net)  , which wmri.net) . The fir2.3.We used a sliding window to analyze the temporal dynamic characteristics of rs-fMRI indices. Hamming windows with a length of 32 TR and a step of 4 TR were applied along the time series. Previous studies have shown that dynamic functional connectivity fluctuations can be captured even with window lengths ranging from 30 to 60 s, and different window lengths do not produce significantly different results . MoreoveALFF/fALFF: ALFF is defined as the average power spectrum of the time series after Fourier transform in a particular low-frequency band , and fALReHo: A method for quantifying functional separation using Kendall’s coordination coefficient to calculate time series between a particular voxel and its nearest neighboring voxels (26 voxels) . ReHo reDC: A method of determining functional integration by calculating Pearson’s correlation coefficient between the time series of each voxel and all gray matter voxels. DC was defined as the sum of positive functional connectivity above the threshold of 0.25 .GSC: Pearson’s correlation coefficients were used to calculate the correlation between each voxel time course and the average time course from global signals . Then, FVMHC: The average T1 mirrored images were first used as a template to transform the functional images to the standard symmetric space. Then, Pearson’s correlation coefficients between the time course of each voxel and the corresponding symmetric hemispheres were calculated to represent the functional connectivity between any pair of symmetric hemispheres . FinallyThe fMRI indices mentioned later in the text referred to dynamic measures, namely, dynamic fALFF/ReHo/DC/GSC/VMHC.2.4.Kendall’s W coefficient was used to quantify the degree of concordance among five dynamic rs-fMRI indices , since this non-parametric statistic has no assumptions of the distribution and is insensitive to differences in scale among these fMRI measures . Due to 2.5.The demographic information was assessed with SPSS 26.0. The baseline data of the ADHD-C, ADHD-I and TD groups were checked for normality and homogeneity of variance, and the group comparisons were corrected by Bonferroni adjustment for multiple comparisons. The statistical results are shown in p < 0.001 and survival after 5,000 Monte-Carlo simulations. In addition, a post hoc analysis was performed for clusters with significant interaction effects. The significant clusters were extracted as regions of interest (ROIs), and simple effect analysis was performed between two frequency bands and among three groups, respectively, in SPSS. The statistical results were corrected by Bonferroni adjustment. Then, we calculated the mean of a given fMRI measure  in the ROIs for each time window. In each participant, we calculated the correlations between pairs of the time series of five fMRI indices. These correlations were then averaged across each group of participants to provide an estimation of the dynamic correlation. Finally, Pearson’s partial correlation analysis was conducted to examine the relationships between the concordance indices of the ROIs and clinical scores, with age and FD as covariables.We constructed a full-factorial analysis of variance (ANOVA) model with frequency band (slow-5 and slow-4) as the within-subject factor and group  as the between-subject factor in SPM12. The interaction effects of frequency and group on the concordance maps were compared at the voxel-based level. The mask was constructed from the gray matter of 90% of included individuals, and age and FD were included as covariables. Monte Carlo simulations were performed to correct the resulting images, and the cluster threshold was set at a voxel-wise 3.3.1.post hoc analysis showed that the between-group differences all occurred in slow-5 (with Bonferroni correction), while no significant differences were found in slow-4 .As shown in n slow-4 . The ADHn slow-4  that bot3.2.p = 0.033) (p = 0.693).No significant differences in volume-wise concordance were found among the ADHD-C, ADHD-I, and TD groups. However, the ADHD-C group showed an increased standard deviation (SD) of volume-wise concordance in slow-5 compared with slow-4 (= 0.033) , while t3.3.The ROI-level correlations between pairs of five fMRI indices  are shown in 3.4.r = −0.467, −0.531; p = 0.033, 0.013; uncorrected). The voxel-wise concordance of the MCC was negatively correlated with ADHD Index and Inattentive Index of the ADHD-I group .The significant partial correlation analysis results were all in slow-5 . The vox4.post hoc analysis showed prominent temporal variation among the three groups in the slow-5 band, and the voxel-wise concordance was also correlated with clinical scores. The volume-wise concordance showed no significant group differences, but showed differed between the slow-5 and slow-4 frequency bands.This study compared the temporal and spatial concordance of dynamic rs-fMRI indices among the ADHD-C, ADHD-I and TD groups in the slow-5 and slow-4 bands. The results showed that the voxel-wise concordance of the MCC and SMA was significantly influenced by the interaction of frequency band and group. Moreover, the 4.1.By analyzing the spatiotemporal coupling states of ADHD-C and ADHD-I subtypes, we found that the ADHD-I group exhibited lower temporal concordance than the ADHD-C group in the MCC and SMA. Dynamic concordance states reflect the integrated function of brain network segregation and integration, and are related to functional connectivity within and between networks . The SMADynamic analysis revealed that all fMRI measures were closely coupled with each other in healthy individuals, reflecting common fluctuation patterns underlying the functional aspects . However4.2.Spatial concordance did not differ among the three groups, which is consistent with previous studies in ADHD patients . However4.3.The temporal dynamic concordance significantly differed in the slow-5 frequency band between the ADHD groups, while no significant differences were found in the slow-4 band. The ADHD-C group showed stronger functional concordance in the slow-5 band than in the slow-4 band, while the impaired regions in the ADHD-I group exhibited significantly reduced temporal concordance in slow-5. Although spatial concordance showed no differences among the three groups, there were differences between the slow-5 and slow-4 bands. The increased SD of spatial concordance in slow-5 reflected the increased neuronal activity and fluctuations in the ADHD-I group. The results suggest that frequency subbands may have different pathological importance and that slow-5 is more sensitive to detecting abnormal neural activity among ADHD subtypes than slow-4. Although the sources and meanings of signals from different frequency bands are still unknown, studies have suggested that different subbands are associated with different neural functions and physiological processes . The dif4.4.The correlation analysis showed that in the slow-5 band, the voxel-wise concordance of the MCC was negatively correlated with Hyper/Impulsive Index of the ADHD-C group and Inattentive Index of the ADHD-I group. The correlation with the severity of symptoms implies that the functional concordance of the MCC is associated with phenotypic traits. Both the ADHD-C and ADHD-I patients showed an increase in symptom severity and pathological damage with aggravation of neuronal substrate deterioration. Furthermore, since there were no significant differences in global volume-wise concordance among the three groups, no correlation between volume concordance and symptom scores was found. Our results emphasize the ability of functional concordance to capture the neuropathological features of ADHD subtypes and may provide some insights into the dynamic neural activity patterns of ADHD subtypes.4.5.The study has some limitations. First, the study had a small sample size, and the results need to be verified in larger datasets. Second, the dataset did not include ADHD-HI patients, so we did not conduct a comparative analysis of the three subtypes of ADHD. Third, the correlation between dynamic concordance and clinical scores did not survive correction for multiple comparison, and no multiple-comparison correction was applied for the correlation analysis. Finally, we did not exclude participants with comorbid oppositional defiant disorder, dysthymia or learning disabilities. More experiments are needed to determine whether these disorders may affect the results.5.The study revealed that ADHD-I and ADHD-C patients showed significant voxel-wise concordance differences in the MCC and SMA. Functional concordance in ADHD patients was frequency dependent and showed greater sensitivity to temporal variations in the slow-5 band than in the slow-4 band. The results suggest that the frequency effects should be considered in future research, and frequency-specific temporal concordance provides new insights into the neurophysiological mechanism of ADHD subtypes.Publicly available datasets were analyzed in this study. This data can be found here: ADHD-200 Sample, 1000 Functional Connectomes Project protocols.The studies involving humans were approved by Research Ethics Review Board of Institute of Mental Health, Peking University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.RC and YJ contributed to conception and design of the study. RC performed the statistical analysis and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.


Attention Deficit/Hyperactivity Disorder (ADHD) and Borderline Personality Disorder (BPD) have several similarities and it is difficult to distinguish these disorders in adolescents.We aimed to identify the unique correlates of mentalization abilities that may distinguish these two disorders, and to investigate the mentalization abilities of adolescents with ADHD, BPD and ADHD+BPD in an inpatient sample (n=550) to determine the effect of co-morbidity on mentalization abilities.We have explored the relationship between Child Eye Test (CET) scores, Movie for the Assessment of Social Cognition (MASC) subscales, and ADHD and BPD symptoms in adolescent inpatients. In addition, we compared ADHD, BPD and ADHD+BPD groups in terms of their mentalization abilities.Correct MASC scores were negatively associated with both ADHD and BPD symptoms in girls, and negatively associated with ADHD symptoms in boys. In addition, hypermentalization scores were associated with BPD symptoms in girls, and hypomentalization and no mentalization scores were associated with ADHD symptoms in girls. CET scores were negatively associated with ADHD symptoms in girls, but no relations with BPD were found. Group comparisons revealed no significant difference among groups.We found that while ADHD symptoms are related to hypomentalization, BPD symptoms are rather related to hypermentalization. We believe that these findings make significant contributions to literature aimed at understanding the differences between two disorders which have great commonalities in terms of clinical appearance and developmental course.None Declared


P = 1.41e−16). Nineteen independent loci were jointly associated with both ADHD and parental lifespan, with most of the alleles that increased the risk for ADHD being associated with shorter lifespan. Fifteen loci were novel for ADHD and two were already present in the original GWAS on parental lifespan. Mendelian randomization analyses pointed towards a negative causal effect of ADHD liability on lifespan , although these results were not confirmed by all sensitivity analyses performed, and further evidence is required. The present study provides the first evidence of a common genetic background between ADHD and lifespan, which may play a role in the reported effect of ADHD on premature mortality risk. These results are consistent with previous epidemiological data describing reduced lifespan in mental disorders and support that ADHD is an important health condition that could negatively affect future life outcomes.There is evidence linking ADHD to a reduced life expectancy. The mortality rate in individuals with ADHD is twice that of the general population and it is associated with several factors, such as unhealthy lifestyle behaviors, social adversity, and mental health problems that may in turn increase mortality rates. Since ADHD and lifespan are heritable, we used data from genome-wide association studies (GWAS) of ADHD and parental lifespan, as proxy of individual lifespan, to estimate their genetic correlation, identify genetic loci jointly associated with both phenotypes and assess causality. We confirmed a negative genetic correlation between ADHD and parental lifespan (rg = −0.36,  Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that emerges in childhood and often persists into adulthood, affecting approximately 5.3% of children and adolescents and 2.8% of adults , 2. It iThere is increasing evidence linking ADHD to a shorter life expectancy and mortality rates in individuals with ADHD are two to five times higher than in individuals without ADHD , 5. BesiADHD often co-occurs with other mental and somatic comorbid disorders, traits and behaviors that are likely to increase mortality rates , 11. TheADHD and lifespan are complex traits influenced both by genetic and environmental factors. Heritability is estimated to be around 70–80% for ADHD and 7–16% for human lifespan . Genome-In the present study, we aim to examine the shared genetic architecture and the nature of the relationship between ADHD and parental lifespan, as a proxy for individual lifespan, using available GWAS data on both phenotypes by: (i) estimating their genetic correlation; (ii) performing a cross-trait analysis and (iii) testing the causal role of ADHD on lifespan.http://www.internationalgenome.org/). SNPs that were non-biallelic, without rsIDs, duplicated, or with strand-ambiguous alleles were removed. We also filtered out SNPs with INFO scores <0.9 in the summary statistics files, those mapping to the extended major histocompatibility complex  and the 8p23.1 region , which are prone to rearrangements, SNPs located on the X, Y and mitochondrial chromosomes, and SNPs with sample sizes 5 standard deviations away from the mean. Finally, a common set of 3,206,697 SNPs were kept in both summary statistics. All P values were adjusted for standard genomic control (GC).GWAS summary statistics on ADHD were obtained from Demontis et al., comprising a total of 19,099 individuals with ADHD and 34,194 healthy controls, all of European ancestry . SummaryLinkage Disequilibrium Score Regression (LDSC) was used to calculate genome-wide genetic correlations across the traits studied .P values for the primary trait were plotted conditioning on different association strengths  with the secondary trait. Thus, the visualization of a leftward shift in the primary trait of interest, as a function of increasingly strict P value thresholds in the secondary trait, was an indicator of a shared polygenic architecture between the two traits. To test for SNP-based heritability enrichment of a trait conditioned on different association strengths with a secondary trait, we used stratified LDSC [The shared polygenic architecture between the two traits was assessed by means of stratified cross-phenotype Q-Q plots. ied LDSC . As a reied LDSC .P values for both phenotypes are as small as or smaller than the observed P values”, using pleioFDR (https://github.com/precimed/pleiofdr) [2 = 0.05, kb = 500) using the 1000 Genomes Project Phase 3 European as the reference panel for LD computation and PLINK 1.09 [P < 5e−08) was assessed according to distance (+/−250 kb) and linkage disequilibrium (r2 > 0.1) between of the cross-trait lead SNPs and previous reported hits.To identify genetic loci jointly associated with ADHD and parental lifespan we estimated the conjunction FDR (conjFDR), defined as “the posterior probability that a given SNP is null for both phenotypes simultaneously when the leiofdr) . We keptINK 1.09 , 29. We 2 ≥ 0.6 with one of the independent significant SNPs was performed in FUMA  [th of September 2021, was used to identify traits previously associated with the SNPs of interest and we queried SNPs for known brain eQTLs using the Genotype-Tissue Expression (GTEx) v8 [Functional annotation of all SNPs with a conjFDR value <0.10 and an LD rlab.nl/) . We comblab.nl/) , which plab.nl/) , a categlab.nl/) , the relGTEx) v8  and BRAIGTEx) v8 . We alsoTo assess the causal effect of the genetic liability of ADHD on lifespan, the summary statistics from Pilling et al. , generat2 = 0.05, kb = 500) with PLINK 1.09 [P value threshold of 5e−08 in the ADHD GWAS to be used as instruments. The multiplicative random effects inversed-variance weighted (IVW) was used as the main method to obtain the average effect across genetic variants. For IVW results to be valid, genetic variants used as instruments must meet three assumptions: (i) robust association with the exposure, (ii) absence of horizontal pleiotropy, or association with the outcome through an exposure-independent pathway, and (iii) independence of confounders influencing exposure and outcome. Additional MR methods were implemented, as sensitivity analyses, for significant IVW results (IVW P < 0.05) to assess the robustness of the findings under weaker assumptions: (i) the weighted median method, which under equal weights requires at least half of the variants to be valid instruments and is robust to outliers [2gx < 0.9) MR-Egger causal estimates are biased towards the null, and the type I error of the pleiotropy test can be inflated. For this reason, when I2gx < 0.6, MR-Egger results were disregarded. In addition, heterogeneity tests and leave-one-out analyses were performed and scatter, funnel and forest plots were generated. The analysis in the opposite direction, testing the effect of parental lifespan on ADHD, was considered as a negative control. Finally, as an alternative method, we also used the ρ‐HESS implementation to prioritize putative causal models between pairs of traits, as previously described [Causality between ADHD (as the exposure), and parental lifespan (as the outcome), was assessed by two-sample MR using the TwoSampleMR and MRPRESSO R packages , 40. AftINK 1.09 , indepenoutliers ; (ii) thoutliers . MR-Eggeescribed , 44.2 = 0.05, kb = 500) and, given that no genome-wide significant SNPs were available, a threshold of 5e−06 was chosen to select independent SNPs for these traits.To explore whether a putative causal effect of the genetic liability of ADHD on lifespan is mediated by impulsive personality traits we used MVMR and summary statistics from the GWAS by Sanchez-Roige et al. undertaken in over 20,000 individuals . Out of cause R package [2 = 0.05, kb = 500). The CAUSE method uses a more permissive threshold for variant selection than MR (P < 1e−03). In addition, CAUSE allows all variants to show uncorrelated pleiotropy, also accounted for by MR-Egger or MR-PRESSO, but it also allows a subset of variants to show correlated pleiotropy, when they affect exposure and outcome through a shared heritable factor. CAUSE compares two nested models by measuring how well the posterior distributions of a particular model fit the data: (i) a sharing model, which only allows for pleiotropic effects and no causal effects; and (ii) a causal model, which also allows both for pleiotropic and causal effects.We also explored causality between ADHD and lifespan using the  package  considerP = 1.41e−16). Partitioning ADHD SNP heritability on different parental lifespan P value thresholds showed enrichment of ADHD SNP heritability  revealed that 92.1% of these loci lay on regions of open chromatin and most of the signals were intergenic, intronic or located in intronic non-coding RNA (ncRNA) genes. In addition, several SNPs in these genomic risk loci were likely to affect the binding of transcription factors or had CADD scores >12.37, suggesting high deleteriousness , with alleles that increased the risk for ADHD also shortening lifespan . In line with these results, MR analyses showed evidence of a negative causal effect of ADHD liability on lifespan  or heterogeneity (I2 = 11.15). The results were not driven by a single SNP . MVMR analyses showed that the effect of ADHD liability on lifespan remained unchanged when accounting for total impulsivity score or lack of premeditation, however it decreased (from −0.07 to −0.06) when taking into account the effect of positive urgency, suggesting that positive urgency may be mediating around 11% of the effect of ADHD liability on lifespan (Supplementary Table P = 2.98e−184), and parental lifespan from the latest was also negatively correlated with ADHD .To explore the landscape of pleiotropic effects further, we examined the direction of the effects of the lead SNPs of all independent loci from the cross-trait analysis on both ADHD and parental lifespan and found an opposite direction of effect for 95% of them , neurological  and metabolic disorders  [AKAP6 and SEMA6D genes, previously associated with increased risk of schizophrenia, lower cognitive ability and educational attainment [SYPL2, which encodes a vesicular transmembrane protein that belongs to the synaptophysin family, key elements for the regulation of neuronal synaptic vesicles [HMG20A, a non-histone chromosomal factor that regulates gene expression through changes in chromatin conformation, also involved in the regulation of neuronal differentiation [The pleiotropic risk loci identified were enriched in introns and open chromatin regions, and almost half of them (42%) were cis-eQTLs tags for at least one gene in the brain, supporting their putative causal effect. Among the identified risk loci, we highlight new relevant candidate genes. For instance, the ntenance , which intenance . Interesith ADHD –57. In a stroke) –64, whic stroke) –67. We atainment , all of tainment –72. In avesicles , 74; andntiation , 76.Our results seem to reflect an effect of ADHD on premature mortality risk and are consistent with previous epidemiological data describing a shortened lifespan in many mental disorders , 12. A rOur results, however, should be interpreted in the context of some considerations:First, parental lifespan of genotyped subjects was used as a surrogate trait for individual lifespan in the present generation. This kinship cohort design is supported by previous results reporting that parental lifespan predicts the long-term mortality of their offspring. However, the use of indirect genotypes reduces the effective sample size of the study , 78. Thin = 208,118) further reduced the statistical power and may have led to the inconsistent results observed [P values [Second, to avoid the potential bias introduced by using summary statistics generated through a survival analysis , such asobserved . CAUSE wP values , 81, whiThird, despite the shared genetic architecture underlying ADHD in children and adults , the risIn summary, our results are in agreement with observational studies supporting ADHD as the entrance into a detrimental life trajectory, support negative genetic correlation between ADHD and lifespan and show common genetic loci shared between them, most of which showed risk-increasing effects on ADHD and reduced overall life expectancy. These results confirm the general pattern of increased mortality rates and reduced overall life expectancy associated with ADHD and highlight the need for further studies on larger datasets to better understand the common genetic architecture underlying these complex relationships.SupplementSupplementary Tables


Social isolation in childhood can be detrimental to physical and mental health. Children with neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD), may be particularly at risk for becoming socially isolated. Similarly, isolated children have limited opportunities to observe, model, and learn age-appropriate interpersonal interactions with other children which could increase ADHD behaviours.This study examined longitudinal associations between ADHD symptoms and social isolation across childhood. We tested the direction of this association across time, while accounting for pre-existing characteristics, and assessed whether this association varied by ADHD presentation, informant, sex, and socioeconomic status.Participants included 2,232 children from the Environmental Risk (E-Risk) Longitudinal Twin Study. ADHD symptoms and social isolation were measured at ages 5, 7, 10, and 12. We used random-intercept cross-lagged panel models to assess the directionality of the association across childhood.Children with increased ADHD symptoms were consistently at increased risk of becoming socially isolated later in childhood, over and above stable characteristics (β=0.05-0.08). These longitudinal associations were not bidirectional; isolated children were not at risk of worsening ADHD symptoms later on. Children with a hyperactive ADHD presentation were more likely to become isolated, compared to an inattentive presentation. This was evident in the school setting, as observed by teachers, but not by mothers at home.Our findings highlight the importance of enhancing peer social support and inclusion for children with ADHD, particularly in school settings. We add explanatory value over and above traditional longitudinal methods as our results represent how individual children change over time, relative to their own pre-existing characteristics.None Declared


Bartonella spp. may cause intra-endothelial infection and skin manifestations caused by these bacteria may be diverse, including leukocytoclastic vasculitis and ulcers.Livedoid vasculopathy (LV) manifests as ulcers and atrophic white scars on the lower extremities. The main known etiopathogenesis is hypercoagulability with thrombus formation, followed by inflammation. Thrombophilia, collagen and myeloproliferative diseases may induce LV, but the idiopathic (primary) form predominates. Bartonella spp. in patients with difficult-to-control chronic ulcers diagnosed as primary LV.The aim of this study was to investigate the presence of bacteremia by Questionnaires and molecular tests  were applied and liquid and solid cultures were performed in the blood samples and blood clot of 16 LV patients and 32 healthy volunteers.Bartonella henselae DNA was detected in 25% of LV patients and in 12.5% of control subjects but failed to reach statistically significant differences (p = 0.413).Bartonella spp. infection.Due to the rarity of primary LV, the number of patients studied was small and there was greater exposure of the control group to risk factors for B. henselae was detected in one of every four patients, which reinforces the need to investigate Bartonella spp. in patients with primary LV.Although there was no statistically significant difference between the groups, the DNA of  Livedoid vasculopathy (LV) manifests as painful ulcers located distally on the lower limbs, which slowly develop into whitish atrophic lesions, with punctate telangiectasias, brownish pigmentation, accompanied by livedo racemosa. The most often accepted LV etiopathogenesis comprises vaso-occlusive phenomena resulting from hypercoagulable states. Conditions such as thrombophilia, diffuse connective tissue diseases , myeloproliferative disorders, and blood stasis are known to be associated with the disease, but the idiopathic (primary) form is the predominant one.2Bartonella spp. was suggested. Bartonella henselae DNA was detected by polymerase chain reaction (PCR) in the patient's blood and there was a prompt improvement of the chronic ulcers with treatment of the B. henselae infection with doxycycline 200 mg/day for one year. After treatment withdrawal, however, the LV lesions recurred. The bacterium was isolated and its DNA was once again detected in a new blood sample  and in tubes without anticoagulant.A free and informed consent form was obtained from each participant after evaluation and authorization provided by the UNICAMP Institutional Research Board (CAAE: 48163015.8.0000.5405). Patients over 18 years of age, with a diagnosis of primary LV undergoing follow-up at UNICAMP Dermatology Outpatient Clinic were included in the study, and volunteers without clinical complaints were included as a control group . Each participant answered a questionnaire to assess risk exposure for acquiring Bartonella spp. and species-specific double-amplification (nested) and real-time PCRs for B. henselae were performed. Three controls were added in all procedures, . Additionally, dilutions of B. henselae were tested in each reaction to determine the sensitivity and the limit of detection of each PCR. All of these methods have been previously described.The laboratory where the analyses were performed follows standards to ensure the quality control described by Pitassi et al.p = 0.001), with a mean age of 26.71 years, whereas the mean age was 37.62 years among the patients. The control group had more men than the patient group (p = 0.048). Individuals in this group had more contact with pets (p = 0.007), were more bitten and/or scratched (p = 0.000), and were more exposed to arthropods (p = 0.003). There was no significant difference (p > 0.05) when comparing these characteristics in those who had Bartonella spp. DNA detected and those who did not, in each of the groups separately.Sixteen patients with primary LV and 32 individuals without clinical complaints participated in the study. All answered a questionnaire whose data are shown in The constitutive gene (GAPDH) was detected in all samples from patients and volunteers, guaranteeing the quality of the DNA extracted for the analyses and the absence of inhibitors for the reactions, guaranteeing the quality of the DNA extracted from the samples and the absence of inhibitors for the molecular reactions.Bartonella spp., the limit of detection was 50 genome equivalent (GE) and ten GE in the nested PCR and real-time PCR.In conventional PCR for B. henselae DNA was detected in four patients in at least one species-specific reaction. In the control group, DNA was detected in four volunteers. Amplified material from six participants out of eight with bacterial DNA detection was sequenced, and all amplified samples showed 99% to 100% of similarity to B. henselae (GenBank accession number: CP020742.1).Bartonella spp. in solid cultures from any sample of the 48 participants. There was no statistical significance using Fischer’s test in the detection of bacterial DNA between the two groups (p = 0.413), as also observed in There was no growth of Considering that primary LV is a rare manifestation and using Fisher's exact test to establish the sample size calculation for the study and setting the significance level at 5%  with a sampling power of 80% (beta or type II error of 20%), based on the obtained data, it can be established that the minimum sample size for the results to have statistical significance would be 106 patients.Bartonella spp. in LV. As it has been proposed for SARS-CoV-2,Bartonella spp. also causes direct vascular damage or promotes a hypercoagulable state.The authors did not find any studies in the literature reporting infection by Bartonella spp. infection.B. henselae.19It is known that LV is a vaso-occlusive disease with hyalinization and thrombosis of dermal vessels, and many patients complain of pain and paresthesia. All studied patients reported severe pain associated with the presence of ulcers, and most reported paresthesia or mild pain after healing.Bartonella spp. in three-quarters of the patients, which is not surprising, since LV is a cutaneous manifestation of different diseases. Moreover, B. henselae causes cyclic bacteremia, and the blood samples can be obtained at intervals when the bacteria are not circulating. Different authors have shown that infection by Bartonella spp. has a greater chance of being diagnosed through a biopsy of tissue fragments than by a blood sample and that the results of this difference may be of greater importance and show statistical significance.It was not possible to detect the DNA of Bartonella spp. infection and false-negative results are frequent, even when using a platform with molecular and microbiological techniques. The need for multiple tests for adequate detection of this microorganism, aiming at reducing false negative results, has already been discussed by Drummond et al. Molecular techniques are limited by their low sensitivity .Bartonella species; however, as the stain is non-specific, the reading can be difficult due to the formation of silver precipitates. A study by Caponetti et al. evaluated the use of immunohistochemistry (IHC) for the identification of B. henselae in cat-scratch disease (CSD) in 24 cases of lymphadenitis in formalin-fixed and paraffin-embedded biopsy specimens and compared the results with silver impregnation (SI) and PCR. The positive cases were as follows: SI, 11 (46%); PCR, nine (38%); and IHC, six (25%). Only two cases (8%) were positive for all three studies and SI was more sensitive but less specific. Although 11 (46%) of the study cases were positive using SI, only six of these samples were concurrently positive at the immunohistochemical analysis and/or PCR. This finding suggests that several of the cases interpreted as positive by SI may represent false-positive results and that the diagnostic sensitivity of these three tests is low for CSD.Bartonella spp. As the infectious process can be localized, a negative immunohistochemical result does not definitively rule out the diagnosis of endocarditis by Bartonella spp.Bartonella spp. is difficult, even under specific and ideal conditions.Furthermore, there is no reference diagnostic method for Bartonella spp. was observed in any of the study participants. The difficulty of treating the infection with antibiotics, as seen in trench fever (caused by Bartonella quintana) and Oroya fever , suggest that these bacteria are not eradicated. This potential persistence of the infection makes it impossible to guarantee that manifestations observed in LV are infectious or post-infectious immune reactions, as has been suggested.Bacteremia was documented in the index case, through the isolation of the bacteria in a solid medium even after one year of treatment with doxycycline, but no isolation of Bartonella spp. This is due to the fact that isolation depends on specific media and prolonged incubation. Additionally, fastidious growth and lack of success in primary isolation make this type of diagnosis unfeasible.Few research groups perform solid cultures for the isolation of Bartonella spp. could be just one of the causes of this manifestation. Moreover, because it infects endothelial cells and is the only known genus of bacteria to stimulate its proliferation in humans,As LV is the cutaneous manifestation of different diseases, infection by Bartonella spp. infection, factors that were observed in a study carried out with blood donors from the UNICAMP Blood Bank,B. henselae in patients is higher and that these bacteria can, in fact, be related to disease onset. Another explanation for not having found a statistically significant difference is the small number of patients in the study that evaluates patients with a rare disease, which was carried out as a pilot study in a single institution. As described in the results, using volunteers with the same characteristics as the controls, would take almost seven times more patients to document statistical significance.Data collected from the questionnaire regarding the risk factors for bartonellosis  showed tBartonella spp., reinforces the need to use diverse and complementary methods to increase the sensitivity and accuracy of the diagnosis. This combination of methods and the testing of different samples makes the laboratory diagnosis more effective and has been used by different research groups.30The lack of a reference diagnostic test, combined with the great difficulty in detecting bacteremia caused by B. henselae DNA was detected in one out of four patients with primary LV, which reinforces the need to investigate Bartonella spp. infection in these individuals, although there was no statistical difference between the patient group and the control group.National Council for Scientific and Technological Development (CNPq) for the scholarships granted: n. 170501/2018-3 (LSS), n. 313762/2021-0 (PENFV) and n. 151006/2021-0 (MRD). São Paulo Dermatology Support Fund (Funadersp) / Brazilian Society of Dermatology, Regional São Paulo .Marina Rovani Drummond: Approval of the final version of the manuscript; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.Luciene Silva dos Santos: Approval of the final version of the manuscript; design and planning of the study; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.Lais Bomediano Souza: Approval of the final version of the manuscript; drafting and editing of the manuscript; critical review of the manuscript.Gabriela Nero Mitsuushi: Approval of the final version of the manuscript; design and planning of the study; critical review of the literature; participation in patient selection and sample collection.Maria Letícia Cintra: Approval of the final version of the manuscript; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.Andrea Fernandes Eloy da Costa França: Approval of the final version of the manuscript; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.Elemir Macedo de Souza: Approval of the final version of the manuscript; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.Paulo Eduardo Neves Ferreira Velho: Approval of the final version of the manuscript; design and planning of the study; participation in patient selection and sample collection; drafting and editing of the manuscript; critical review of the literature; critical review of the manuscript.None declared.


Children with attention-deficit hyperactivity disorder (ADHD) exhibit executive function deficits, which can be attributed to a dysfunction in the prefrontal region of the brain. Our study aims to evaluate the alteration of brain activity in children with ADHD during the administration of a go/no-go task using functional near-infrared spectroscopy (fNIRS) in comparison to a control group containing typically developing (TD) children.via fNIRS in the prefrontal lobe.32 children with ADHD and 31 of their TD peers were recruited and asked to perform a go/no-go task while undergoing measurements, with the aim of detecting changes in average oxygenated hemoglobin signaling (Δavg oxy-Hb) fNIRS data showed significant differences between the left and right dorsolateral prefrontal cortices, with a lower Δavg oxy-Hb change in the ADHD group compared to the TD group.Our results indicate that brain dysfunction in children with ADHD is related to functional impairments in the dorsolateral prefrontal cortex. The go/no-go task paired with fNIRS represents a useful measurement tool to assess prefrontal brain dysfunction in children struggling with ADHD. Attention-deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and is characterized by age-inappropriate symptoms of inattention, impulsivity, and hyperactivity with a prevalence of 6.29% in both children and adolescents , even correlation potentials (ERPs), and functional magnetic resonance imaging (fMRI), in addition, to other methods to compare the differences between children with ADHD and those without. . fNIRS is a non-invasive neuroimaging technique that uses near-infrared light to measure changes in oxy- and deoxyhemoglobin (oxygen-Hb and deoxy-Hb) concentrations over time. It is an indirect method of measuring attention control processing throughout the performance of different neuropsychological tasks   and 31 TD children (24 boys and seven girls). Children with ADHD were recruited from patients referred to the Child and Adolescent Psychology Clinic of Ningbo Kangning Hospital. All children with ADHD were previously diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition  in this study. The regions of interest in this study were the right and left dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (mPFC), and temporal lobe (TL). The channels corresponding to each brain region are presented in We investigated inhibition related to hemodynamic activation using a 48-channel near-infrared optical imaging system  operating at three wavelengths (730/808/850 nm) with a sampling rate of 11 Hz. An elastic cap containing 15 light source optodes and 16 light detector probes was arranged, as shown in The go/no-go task was organized into 6-min block sets with a 10-s rest. Each set consisted of alternating go (baseline) and go/no-go (target) blocks . Each blvia NirSpark analysis software using the following method. (1) Unsatisfactory time intervals containing sudden, obvious, and discontinuous noise were excluded; (2) artifacts induced by motion and the environment were corrected ; (3) a band-pass filter (0.01–0.2 HZ) was applied to remove the slow drift induced by physiological and environmental noise; (4) raw optical density values were converted into concentration changes for both oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) through the modification of the Beer–Lambert Law; (5) calculation of the inter-trial mean of differences between oxy-Hb concentration changes during the target periods (4–27 s after the go block onset) in each channel were performed for multiple trials  of go trials and the accuracy (ACC) for go and no-go trials were used as dependent variables. The accuracy for each condition was computed by dividing the correct answer (correct response and appropriate rejection) by the total number of stimuli.t-test or chi-square (χ2) test was used to compare data in each category . All statistical analyses were conducted using the SPSS statistical software package (version 25.0) with a statistical threshold p-value of <0.05.To better compare numerical variables between the ADHD and TD groups, an independent sample The baseline demographic characteristics of each study participant are presented in The average accuracy rates and RTs in each go/no-go task for ADHD and TD participants are summarized in t = −2.364, p < 0.05; left DLPFC t = −2.301, p < 0.05), as shown in t = −0.788, p = 0.43; left mPFC t = 1.849, p = 0.069) and TL , as shown in Differences in oxy-Hb signals measured using fNIRS are presented according to corresponding areas of the brain and participant group. Compared with the TD group, Δavg oxy-Hb changes in subjects with ADHD were shown to be significantly lower in both right and left DLPFC compared to the TD group , and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.TW and XL: experimental design, data collection, data processing, and manuscript writing. FC: data collection. CL and SW: test task writing and data processing. WZ and DZ: experimental design and project implementation management. All authors contributed to the article and approved the submitted version.


Adults with Attention-Deficit/Hyperactivity Disorder (ADHD) are generally distractible. Yet, the precise relationship between ADHD and distractibility remains under-specified in two respects. First, different sources of distraction, such as background noise or mind wandering, may not be equally associated with ADHD. Second, ADHD itself comprises a variety of symptoms that show considerable heterogeneity and it is unclear which ADHD symptoms are associated with which type of distraction.N = 69) and two large non-clinically evaluated samples . In all samples, participants completed questionnaires about their susceptibility to external distraction, unwanted intrusive thoughts, spontaneous mind-wandering and ADHD symptomatology.The current study addresses these questions using one clinically evaluated sample (Traditional regression and novel network analyses revealed an overwhelming contribution of spontaneous mind-wandering in explaining ADHD symptoms, although external distraction and unwanted intrusive thoughts were also associated with a small number of ADHD symptoms.Findings support a growing body of literature linking spontaneous mind-wandering and ADHD, and they highlight the heterogeneity in the association between ADHD symptoms and different sources of distraction. These sIndividuals with a high level of ADHD symptomatology are often generally described as being more distractible . Yet, th1.1.While the word “distraction” can be used to refer to both external and internal sources, the study of distraction has traditionally focused on external stimulation. As a result, many studies of distractibility in ADHD have focused on this particular type of distraction. Indeed, “being easily distracted by extraneous stimuli” is a common complaint by people with a high level of ADHD symptomatology, and external distractibility has long been considered as one of the diagnostic criteria of ADHD . StudiesExternal stimulation, however, is not the only source of distraction. Studies have shown that performance can be impaired by internal sources of information in the absence of external distraction , 16. In r = 0.62 ~ 0.68 at the latent level, indicating that they are indeed highly related yet distinct forms of distraction. Furthermore, a handful of studies have found that unwanted intrusive thoughts are correlated with inattentive-ADHD symptoms but not with hyperactive/impulsive-ADHD symptoms  . Like misymptoms , 27. ThuSusceptibility to external and internal distractions in ADHD may be based on different neural circuits that are involved in filtering different types of distraction. Bottom-up attentional processing involves the automatic allocation of attention to sensory information in the environment . To avoi1.2.Most studies of external distraction, mind-wandering and unwanted intrusive thoughts have focused on explaining ADHD measured by aggregate scores. Although some studies have examined heterogeneity at the ADHD subtype level  generally, these studies measure ADHD by summing the number of symptoms present, averaging across symptom ratings, or simply comparing individuals with a clinical diagnosis versus healthy controls , 31. By Adult ADHD Self Report Scale (ASRS) scores) can be accounted for by a “general distractibility factor” (d-factor) that is composed of external distraction, unwanted intrusive thoughts, and spontaneous mind-wandering. This seems to suggest that ADHD symptoms can best be described as a susceptibility to general distraction. However, this result was derived from examining the link between two general constructs, general distractibility and ADHD symptomatology, by extracting the common variance shared across different types of distraction and the common variance shared across different ADHD symptoms. As such, it remains unclear if different symptoms of ADHD may be related to different forms of distraction. Consider the following scenarios: Say the symptom often forgetful has a strong association to spontaneous mind-wandering, but often fidgets has only a moderate association to external distraction. By contrast, imagine that both often forgetful and often fidgets are equally strongly related to both spontaneous mind-wandering and external distraction. When using a latent factor approach, these symptoms would be summed together to extract ADHD as a whole. In doing so, the nuance inherent in individual symptoms is lost. That is, we are unable to distinguish if symptoms are equally or differentially related to distractions, because in both cases the overall relationship between the two aggregate scores (distractibility and ADHD symptomatology) will be high.For example, Zhang et al. , used a suicidal ideation, while female participants were at increased odds of experiencing issues in focusing, sleeping, and eating to name a few. These findings highlight the importance of examining disorders at the individual-symptom level.There is a well-documented body of research by Fried and Colleagues about Major Depressive Disorder (MDD) which serves as a good analogy. Their studies uncovered that individual depressive symptoms were differently associated with functional impairment  versus dpoor follow through, task avoidance and difficulty organizing to be significantly associated with academic impairment in adolescents according to teacher ratings. By contrast, in early childhood they found other inattentive symptoms such as forgetful and careless mistakes to be significantly associated with academic impairment according to teacher ratings. These results highlight heterogeneity within symptom dimensions in relation to impairment, as well as changes across development. Other studies assessing ADHD from the individual symptom level have found certain symptoms increase the odds of a clinical diagnosis, such as careless mistakes, difficulty sustaining attention and physically restless to name a few . We intend to better understand the diversity of the relationships between ADHD and distractibility. Let us first have a discussion of the complementary methods used to carry out this symptom analysis.1.3.1.3.1.compare the predictive strength among the predictors. That is, it does not allow one to assess the relative importance of a predictor. This drawback, however, can be easily overcome by a dominance analysis. A dominance analysis examines the dominance of a predictor over another predictor by comparing their incremental R2 contributions across all subset models  with their relationships being represented by edges . The strength of the connection, or edge weight, may differ, with thicker lines indicating stronger relationships and thinner lines indicating weaker relationships. Edge weights may also be positive  or negative . In a psychological network, the edges usually represent partial correlations, that is, the association between two nodes given all other nodes in the network. Compared to the dominance analysis approach, a clear advantage of the network approach is that it can simultaneously estimate relationships among multiple variables of interest. Thus, two nodes that are connected with each other indicate unique associations between the two variables that cannot be explained by other variables in the network. Conversely, two nodes that are not connected with each other  in the network are conditionally independent, meaning that their relationship can be accounted for by other variables in the network. The ability to model conditional independencies makes network analysis useful for exploring potential causal mechanisms , and someone who has difficulties keeping attention when doing boring, repetitive work also makes careless mistakes (ASRS item 7). On the other hand, the same person who is susceptible to spontaneous mind-wandering may not tend to have difficulties unwinding and relaxing themselves (ASRS item 14), but this symptom may be instead associated with other variables in the network. In addition to the observation of individual edges, several well-established metrics are available to quantify the overall importance of a node in the network  (SCID-5-RV) in the clinically evaluated sample, and the Adult ADHD Self Report Scale (ASRS) in the non-clinically evaluated samples. To analyze our data, we implemented a rigorous methodological approach where we used a combination of dominance analysis and network analysis to identify any symptoms that are not equally associated with each type of distraction, but rather are more related to a specific type of distraction.The goal of the present study is to characterize the association between individual ADHD symptoms and three types of distractions . We examined one clinically evaluated sample (N = 69) and two large non-clinically evaluated cohorts  in which participants self-reported their susceptibility to external distraction, spontaneous mind-wandering and unwanted intrusive thoughts using multiple questionnaires to define each distraction construct. Additionally, we measured ADHD symptoms using the 2.2.1.2.1.1.Prolific.co. All participants were native English speakers, between the age of 18–35, living in the U.S. or Canada, and completed at least 50 previous Prolific.co submissions at a 95% approval rating. Participants were removed if they failed 2 (of 4) attention checks. Participants were compensated $3.67 for completing distraction and ADHD symptomatology online questionnaires that took an average of 24 min. One participant was excluded from data analysis for a duplicated IP address. The final sample consisted of 651 participants. The mean age was 26.9 with a SD of 4.98. The sample was 45.31% female and 67.7% Caucasian. This dataset originates from Zhang et al.  attention checks. Therefore, the final sample consisted of 569 individuals. The mean age was 18.83 with a SD of 1.15. The sample was 66.26% female and 56.8% Caucasian. This dataset originates from Zhang et al. (SCID-5-RV) and filling out 15 online questionnaires regarding distraction and ADHD symptomatology (see below for details). Additionally, two participants indicated a comorbid diagnosis of depression (with ADHD). In that ADHD and MDD are highly comorbid, both of these participants were retained  of the retained . The mearetained , 51.2.2.2.2.1.2.2.1.1.SCID-5-RV is structured to assess and diagnose adults with ADHD and was administered by formally trained graduate students and research assistants. The module is made up of 5 major criteria. Criterion A assesses inattentive (questions 1–9) and hyperactive–impulsive (questions 10–18) symptoms. For example, “...have you often missed important details or made mistakes at work (or school) or while taking care of things at home?” Each item is rated on a 3-point scale, ranging from 1 “absent or false” to 3 “threshold or true.” This module uses a dichotomous scoring method that indexes whether a symptom is present or not. Only responses of “threshold or true” are coded as symptom present, while responses of “subthreshold” or “absent or false” are coded as symptom absent. To move on to subsequent sections, a participant must endorse at least 5 inattentive symptoms or at least 5 hyperactive–impulsive symptoms. Criterion B assesses when symptoms first emerged; they must have been present by 12 years of age. Criterion C assesses where symptoms emerge; to continue the survey, participants must endorse symptoms appearing in two or more areas of their life . Criterion D examines severity of symptoms; participants must endorse that their symptoms seriously impair functioning in everyday settings . Finally, Criterion E investigates if a separate psychotic disorder is present. This section aims to ensure that the endorsed ADHD symptoms are not better described by psychotic disorder(s) (if any present). Participants are considered to have a clinical diagnosis of ADHD if they fit Criteria A-E. In that our research question seeks to understand the individual symptoms of ADHD, our analyses consider their dichotomized scores of each individual symptom  from this diagnostic measure . Each question is rated on a 5-point scale, ranging from 0 “Never” to 4 “Very often.” In that our research question seeks to understand the individual symptoms of ADHD, these continuously scored individual items were not summed together  of negative thoughts. Overall, the effective suppression components and intrusion components were the only items included in the final analyses. The final score is generated by summing the scores of all items , for example “think about a recent situation, wishing it had gone better.” Each item is rated on a 4-point scale from 1 “almost never” to 4 “almost always.” The final score is generated by summing the scores of all items. Participants were administered the entire scale, but only the brooding subscale (RRS-B) was included in data analysis, in that the brooding subscale focuses on negative thinking, while the reflection subscale better assesses adaptive features, such as coping with rumination  and ADHD symptomatology. Questionnaires were presented in a pseudo-random order such that questionnaires were categorized into 4 categories pertaining to: (1) external distraction, (2) spontaneous mind-wandering, (3) unwanted intrusive thoughts and (4) ADHD symptoms. These 4 categories were presented in a randomized order. Also, the order of the individual questionnaires within each category was randomized. However, in the ADHD category, the functional impairment questions are based on an individual’s current ADHD symptoms. Therefore, the functional impairment questions were always presented directly after the ASRS in that the ASRS assesses ADHD symptomatology.All study procedures, across the three samples, were conducted online. Participants were provided a SCID-5-RV to assess diagnosed ADHD, which was always completed after the online questionnaires via a password-protected Zoom meeting. Study procedures in the clinically evaluated sample took approximately 1 h. After all study procedures were completed, participants were debriefed and rewarded subject pool credit or compensated based on the respective sample to which they belonged.After answering all questionnaires, participants provided their demographic information. Study procedures for the two large non-clinically evaluated samples took approximately 30 min to complete. Additionally, the clinically evaluated sample participated in a formal clinical interview using the 2.4.Data analysis was conducted in the R (Version 4.0.3) environment. Scale items were reverse coded as necessary, so that higher scores always indicate higher levels of distraction. Normality was found to be satisfactory for all scale variables in all three samples.The current study administered multiple scales for each distraction type. Because the primary focus of the current study is individual ADHD symptoms, for the clarity of the results we created composite scores to represent each distractor type  by averaging the z-scores of scales under each categorydominance analysis package. For each dominance analysis, the outcome variables were the respective ADHD symptom and the predictor variables were the three types of distraction. To evaluate the robustness of the results, we performed each dominance analysis on 1,000 bootstrapped samples using the bootdominanceanalysis function . We examined the proportion of iterations that successfully replicate the original results  in the University Sample, which produced an alpha of 0.59; see 3.2.complete dominance simultaneously over the two other types of distraction . As an example, spontaneous mind-wandering completely dominates both external distraction and unwanted intrusive thoughts for the symptom difficulty sustaining attention (ASRS 8/SCID 2) in all three samples.We conducted a dominance analysis for each of the 18 ADHD symptoms where predictors consisted of composite scores of spontaneous mind-wandering, external distraction and unwanted intrusive thoughts. The results of these dominance analyses are shown in difficulty sustaining attention) as well as several items associated with the hyperactivity/impulsivity aspect of ADHD .As shown in physically restless (ASRS 13/SCID 12), interrupting others (ASRS 18/SCID 18), and trouble waiting (ASRS 17/SCID 17) in the two larger samples but not in the clinically evaluated sample. Second, mind-wandering was the dominant predictor of distracted by extraneous stimuli (ASRS 11/SCID 8) in the clinically evaluated sample but not in the two larger samples. Additionally, we find these results to be especially robust in that the replication rate ranges from 70 to 100% in most of the bootstrapped dominance analyses across all samples.While the dominance pattern of spontaneous mind-wandering was largely consistent across samples, several discrepancies can be found when comparing the results of the clinically elevated sample against those of the two larger samples. First, mind-wandering was the dominant predictor of difficulty relaxing (ASRS14). Lastly, external distraction completely dominated both spontaneous mind-wandering and unwanted intrusive thoughts for the symptom distracted by extraneous stimuli (ASRS 11/SCID 8) with replication rates of 100% in both non-clinically evaluated samples.External distraction and unwanted intrusive thoughts also showcase instances of complete dominance. Across both non-clinically evaluated samples, unwanted intrusive thoughts completely dominated external distraction  and spontaneous mind-wandering  for the symptom inability to do things quietly (SCID 13)  and trouble waiting (ASRS 17/SCID 17) . Interestingly, physically restless (ASRS 13/SCID 12) showcases similar levels of significant contribution from both mind-wandering and external distraction, and both types of distraction completely dominate unwanted intrusive thoughts. However, we only denote an asterisk in often leaves seat (ASRS 12/SCID 11), talks excessively (ASRS 15/SCID 15) and interrupts others (ASRS 18/SCID 18) in the clinically evaluated sample. Therefore, interpreting the dominance results is not necessary for these symptoms in this sample.In the clinically evaluated sample, unwanted intrusive thoughts completely dominated external distraction and spontaneous mind-wandering for the symptoms Overall, these dominance analysis results, across all three samples, support the notion that mind-wandering, overwhelmingly, is more closely associated with individual symptoms of ADHD. Ultimately, these results reveal a robust sense of heterogeneity among symptoms of ADHD in how they relate to different forms of distraction (see 3.3.3.3.1.ASRS items) and 3 dimensions of distraction (derived from composite scores from the respective distraction questionnaires).3.3.2.strength, closeness, and betweenness. The results, which can be found in the strength retained a satisfactory level of stability. Thus, we opted to report the results for strength in the main text and those for closeness and betweenness in the Strength indicates how strongly a given variable is conditionally associated with other variables in the network. It is computed summing up the absolute edge weights for each node. A higher strength value, therefore, indicates that the variable has overall a stronger connection with other variables in the network.First, we examined the network’s topological structure by computing node centrality indices. Of note, Epskamp et al.  recommenstrength are shown in Physically restless (ASRS 13) appears to be the most central node in both the Prolific and University samples. Of the distraction types, spontaneous mind-wandering (MW) appears to be the most central node across both samples. We further examined this intuition by conducting bootstrapped difference tests using the procedure described in Epskamp et al.  is significantly different from the edge weight between the exact same ASRS item edge and another dimension of distraction (example: ASRS 8–EXT).Our research question can be further examined by assessing edge-weight differences. For example, we can assess if the edge weight between an Prior to assessing edge-weight differences, we examined the accuracy of the edge-weights . In shorWe compared the edge-weights by examining if the confidence intervals of the two edge-weights overlap. Non-overlapping confidence intervals indicate a significant difference between the edge-weights. ASRS item 8 (difficulty sustaining attention) showcases a significantly stronger association to spontaneous mind-wandering  across both the Prolific and University samples. ASRS item 9 (not listening when directly spoken to) showcases a significantly stronger association to spontaneous mind-wandering  in the Prolific sample. However, in the University sample, ASRS item 9 (not listening when directly spoken to) only showcases a significantly stronger association to spontaneous mind-wandering when compared to external distraction (but not unwanted intrusive thoughts). ASRS item 13  reveals a significantly stronger association to spontaneous mind-wandering only when compared to external distraction (but not unwanted intrusive thoughts). Across both the Prolific and University samples, we find ASRS item 11 (distracted by extraneous stimuli) to have a significantly stronger connection to external distraction (compared to spontaneous mind-wandering and unwanted intrusive thoughts). Along similar lines across both samples, we find ASRS item 14 (difficulty relaxing) to have a significantly stronger association with unwanted intrusive thoughts .4.In the present study, we tested whether the 18 symptoms of ADHD are differentially related to spontaneous mind-wandering, external distraction and unwanted intrusive thoughts in one clinically evaluated sample and two large non-clinically evaluated cohorts. Participants self-reported their susceptibility to external distraction, spontaneous mind-wandering, unwanted intrusive thoughts and ADHD symptoms. We utilized a combination of dominance analysis and network analysis to identify any symptoms that are not equally associated with each type of distraction, but rather are more related to a specific type of distraction while simultaneously accounting for the other forms of distraction.sustaining attention) as well as several items associated with the hyperactivity/impulsivity aspect of ADHD . These results appear to support previous notions that spontaneous mind-wandering is strongly associated with both inattentive and hyperactive symptoms of ADHD  may serve as a mediator between spontaneous mind-wandering (MW) and many ASRS items, such as careless mistakes (ASRS 7), task avoidance (ASRS 4) and poor follow through (ASRS 1) to name a few. This could potentially explain why in the dominance analysis results, these individual symptoms of careless mistakes (ASRS 7), task avoidance (ASRS 4) and poor follow through (ASRS 1) all showcase spontaneous mind-wandering as completely dominating the other forms of distraction. But in actuality, these symptoms may only be related to spontaneous mind-wandering indirectly, through difficulty sustaining attention (ASRS 8). In other words, when difficulty sustaining attention (ASRS 8) is included simultaneously in a model with particular symptoms, such as careless mistakes (ASRS 7), the direct relationship between these particular symptoms and spontaneous mind-wandering disappears. Other potential mediators include: not listening (directly spoken to) (ASRS 9), distracted by extraneous stimuli (ASRS 11), and difficulty relaxing (ASRS 14) to name a few. It is important to note that these data are cross-sectional, so we are not suggesting any concrete causal claims at this point, but rather offering speculation as to why fewer symptoms indicate differential association to the forms of distraction assessed when using network analysis compared to dominance analysis.As mentioned earlier, with network analysis all intercorrelations among variables in the estimated network are taken into account, which may explain why fewer symptoms showcase differential associations among the forms of distraction . This also allows for exploration of potential causal mechanisms within the network. For instance, mind-wandering hypothesis, which suggests irregular default mode network (DMN) brain activity in individuals with ADHD best explains the occurrence of heightened spontaneous mind-wandering. The DMN refers to a system of brain regions that are generally active during resting state activities in which an individual is not focused on carrying out a goal-directed activity, and mind-wandering is such a state . With this emphasis on distraction from extraneous stimuli, we anticipated that external distraction would play a much larger role in explaining symptoms of ADHD. However, composite external distraction was only significantly more associated with the one symptom item that directly asked about being externally distracted in the non-clinically evaluated samples (ASRS 11: “how often are you distracted by activity or noise around you”), but not in the clinically evaluated sample . Perhaps this idiosyncrasy has to do with the difference in sample size. Our clinically, diagnosed sample includes only 69 individuals while our non-clinically evaluated samples both include over 500 individuals. Therefore, in future studies it would be beneficial to recruit a larger sample of clinically evaluated individuals if possible.We found it surprising that external distraction did not significantly explain more symptoms. The literature on ADHD is saturated with experimental studies that aim to understand distractibility within ADHD by using extraneous stimuli in the environment  . Being edifficulty relaxing (ASRS 14) in both non-clinically evaluated samples and the symptom inability to do things quietly (SCID 13) in our clinically evaluated sample across multiple statistical methodologies. We find our results interesting for several reasons. First, we would like to discuss one inconsistency we noticed between one item from the SCID-5-RV and ASRS in terms of ADHD symptomatology. We used the gold-standard, SCID-5-RV to assess ADHD symptoms via clinical interview in our clinically evaluated sample (N = 69), but we used the ASRS questionnaire to assess self-reported ADHD symptomatology in our large non-clinically evaluated samples  in that no validated, participant-facing version of the SCID-5-RV is currently available for large-scale research studies. We noticed after data collection and analysis, that the SCID-5-RV directly assesses the symptom inability to do things quietly . However, the ASRS does not include any items on inability to do things quietly, rather this questionnaire includes an item that assesses difficulty relaxing (ASRS 14: “How often do you have difficulty unwinding and relaxing when you have time to yourself?”). When looking back at DSM-5 symptom criteria, only the inability to do things quietly (when free time arises) is a symptom of ADHD, but not difficulty relaxing , but not between hyperactive/impulsive-ADHD symptoms and intrusive rumination or negative automatic thoughts. In that inability to do things quietly (SCID 13) is considered a hyperactive/impulsive symptom of ADHD, it is intuitive to predict this symptom would not be related to unwanted intrusive thoughts based on these prior studies, but our study finds the opposite. Perhaps our results are different than expected because we directly examined the relationship between the symptom inability to do things quietly (SCID 13) and unwanted intrusive thoughts, while previous studies correlated higher order sub-symptom categories of inattentive-ADHD and hyperactive/impulsive-ADHD symptomatology with negative rumination. This further highlights the utility of the individual symptom approach in that this nuance may be overlooked when utilizing an aggregated approach.Looking past this inconsistency, Jonkman et al.  and MitcN = 569, N = 651). As stated previously, Zhang et al., utilized a common factor approach to understand distractibility in relation to ADHD symptomatology, and found general distractibility  to better explain latent ADHD symptomatology. Our goal differed in that we wanted to assess whether specific symptoms of ADHD might be related to specific types of distraction and found that mind-wandering is closely tied to many other symptoms. As such, our conclusions are not inconsistent with the conclusions of Zhang et al. Additionally, these results are not meant to suggest that external distraction and unwanted intrusive thoughts are not relevant to ADHD. In fact, in our clinically evaluated sample we found those meeting criteria for ADHD  and those not currently meeting criteria for ADHD (controls)  significantly differed in scores of external distraction  and unwanted intrusive thoughts  effect sizes, respectively, indicating that although mind-wandering emerged as the relatively more important form of distraction for many symptoms of ADHD, external distraction and unwanted intrusive thoughts appear to still be relevant for understanding ADHD.We used the same datasets for our large non-clinically evaluated samples as Zhang et al.,   can be found at: The studies involving human participants were reviewed and approved by the University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board (IRB-HSBS). The patients/participants provided their written informed consent to participate in this study.JO and HZ: study design, study implementation, data analysis, writing original draft, and writing review and editing. MC: study implementation and writing review and editing. PS and JJ: study design, study implementation, data analysis, writing original draft, and writing review and editing. All authors contributed to the article and approved the submitted version.This work was supported in part by a grant from National Institute of Mental Health : R21MH129909) to JJ.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


An estimated 3% to 10% of school children meet the DSM-V criteria for ADHD (Attention-Deficit/Hyperactivity Disorder), however, to be over-diagnosed, the rate of children inappropriately diagnosed with ADHD  would have to be larger than the number of children with ADHD who are under-identified and not diagnosed . Accordingly, a number of investigators take the position that under-treatment with psychostimulants, especially in children and adolescence, will result in continued ADHD symptomatology including future Substance Use Disorder (SUD). However, other researchers and clinicians believe otherwise and espouse laudable arguments for caution and prolonged methamphetamine treatment. While there is ongoing controversy of the role of genetics and epigenetics linked to ADHD, it seems clear that a number of dopaminergic genes and their risk polymorphisms act as DNA antecedents impacted by epigenetic induced methylation. Our hypothesis and literature review suggest that one possible solution is to embrace non addictive interventions to induce global dopamine homeostasis. An estimated 3% to 10% of school children meet the DSM-V criteria for ADHD, however, to be over-diagnosed, the rate of children inappropriately diagnosed with ADHD  would have to be larger than the number of children with ADHD who are under-identified and not diagnosed . Based on the review of research on factors that affect diagnostic accuracy and prevalence studies, Sciutto and Eisenberg concluded that claims that ADHD is systematically over-diagnosed cannot be justified  providedKeeping these studies in mind, Boland et al. , in overThe question of over prescription of stimulant medication to ADHD patients has been evaluated in a non-biased study by Jensen et al. -9. They ®). It is well-known that Methylphenidate has 2 chiral centers, but the drug used in therapy consists of only the threo pair of enantiomers. d-threo-Methylphenidate is more potent than the l-enantiomer. Moreover, methylphenidate is prescribed as a racemic mixture that undergoes stereoselective clearance. Methylphenidate is a short-acting stimulant with a duration of action of 1 to 4 hours and a pharmacokinetic half-life of 2 to 3 hours. Maximum drug concentration after oral administration occurs at about 2 hours. Additionally, the drug is absorbed well from the gastrointestinal tract and easily passes to the brain. Importantly, Methylphenidate is efficacious for short- or long-term treatment for children with ADHD. Its mechanism of action is not completely but it is established that the drug affects the release and reuptake of dopamine in the striatum [AOne key area that has been analyzed by toxicologists involves the pharmacokinetics of methamphetamine  test [These studies raise serious questions about who should be prescribed stimulant medication, especially if there is a pre-addiction genetic risk  in ADHD ®) test .It is known that in order to reduce spurious diagnosis and over prescription of stimulant medication for ADHD in young children (as early as preschool) clinical scientists have sought to subtype ADHD into viable classifications. But even this logical approach has met with poor diagnostic outcomes . Using tHTRF1A, and dopamine receptor D4 were recognized as important hub genes among the reported ADHD genes [Undoubtably, ADHD is a clinically heterogeneous disorder of impulsivity, inattention, and hyperactivity with an early onset in children Based on a consensus of the literature, it is prudent to conclude that there is a real need to improve early diagnosis of ADHD toward being less subjective and more biologically objective . SpecifiHD genes .Tools available today certainly have come a long way towards recognizing ADHD as a disease in the US and other countries, but non-subjective diagnosis awaits further research. In particular, because family, twin, adoption, segregation analysis, and molecular genetic studies have shown that ADHD has a substantial genetic component, it would be beneficial to provide the clinician and patient with the informative prediction of a potential predisposition to ADHD by developing a validated ADHD gene panel. In fact, Sullivan and Rudnik-Levin  reviewedIt is our expert opinion that it seems important that physicians, parents, and teachers become educated as to the risks of stimulant medication and be provided with alternative non-stimulant, but effective new medications or non-addicting nutrition-based therapies. An estimated 3% to 10% of school children meet the DSM-V criteria for ADHD, however, to be over-diagnosed, the rate of children inappropriately diagnosed with ADHD  would have to be larger than the number of children with ADHD who are under-identified and not diagnosed . Accordingly, a number of investigators take the position that under-treatment with psychostimulants, especially in children and adolescence, will result in continued ADHD symptomatology including future SUD. However, other researchers and clinicians believe otherwise and espouse laudable arguments for caution and prolonged methamphetamine treatment. While there is ongoing controversy of the role of genetics and epigenetics linked to ADHD, it seems clear that a number of dopaminergic genes and their risk polymorphisms function as DNA antecedents impacted by epigenetic induced methylation. Our hypothesis and literature review suggest that one possible solution is to embrace non addictive interventions to induce global dopamine homeostasis possibly via electronic therapy , and pro-dopamine regulation like KB220 as epigenetic repair.


Attention deficit hyperactivity disorder (ADHD) is a condition that is usually diagnosed in childhood . However studies have shown that there is a propotion of adults with ADHD that did not have the disorder in their early ages and have been diagnosed in adulthood . Symptoms of adult ADHD are often confused with other psychiatric conditions due to the similarities in clinical criteria . Diagnosing Adult ADHD is still challenging due to lack of gold standard instruments , which can interfere with the therapy .Identify the assessement measures that improve the early diagnosis of Adult ADHDA systematic literature search was executed using the PUBMED and GOOGLEscholar databases from 2009 to 2022 exploring the different assessement tools of adult ADHD using the keywords : Adult ADHD – Diagnosis issues – Assessement .Results show diagnosing Adult ADHD can be by using three different assessement methods : clinical interview – ADHD behaviour rating and neuropsychological tests .Clinical interview has a high sensitivity but low speceficity in diagnosing ADHD in adults .Harrison and Al showed the CAARS scale appears to have adequate specificity in a young adult population giving it has validity to identify invalid symptom presentation.The TOVA and the Conners CPT are the two CPTs widely used in ADHD assessment.It is not clear whether the TOVA or Conners CPT has better diagnostic accuracy .Cognitive test batteries dont show specific results in identifying ADHDTo sum up , diagnosing adult ADHD is a challenging situation due to the lack of specifity in the assessemnt tools . This work highlights the necessity of further studies to improve the diagnostic process and the ability to create a universal guidelines . That way , early interventions and treatments can take place and avoid the confusion with other conditions .None Declared