Emotional dysregulation (ED) is now considered as an important symptom of attention deficit hyperactivity disorder (ADHD). It is believed to have a considerable impact on the severity of the disorder, one’s global functioning, and the prognosis. Our research aimed to evaluate and compare ED and cognitive emotional regulation strategies between ADHD and borderline personality disorder (BPD) patients.N = 279 ADHD, N = 70 BPD, N = 60 BPD + ADHD) were assessed with the Emotion Reactivity Scale (ERS), the Cognitive Emotional Regulation Questionnaire (CERQ), The Basic Empathy Scale (BES-A), the Adult ADHD Self-Report Scale (ARSV-v1.1) and the Beck Depression Inventory II (BDI-II). ADHD, BPD and comorbid patients were compared with each other and with samples of controls extracted from already published data.Four hundred six French-speaking outpatients . However, ADHD patients showed a higher use of adaptive cognitive emotional strategies and a lower level of ED than BPD patients. There is increasing evidence of a close link between emotional symptoms and attention deficit hyperactivity disorder (ADHD) –3. SeverStudies have estimated that 34 to 70% of adults suffering from ADHD have difficulties regulating their emotions . These eBesides the usual description of ED in BPD, namely heightened emotional reactivity to environmental stimuli, inadequate cognitive emotion regulation strategies resulting form difficulty identifying, differentiating, and labeling emotions have also been suggested as important components of ED –22. BPD Although several hypotheses explaining ED in ADHD have been suggested, including executive function deficits, social cognitions deficit, impaired emotional perception/processing, lack of empathy and impairment in first and second order theory of mind and poor mentalizing capacities , 23–31, The aim of our study was therefore to compare emotional reactivity, cognitive emotional regulation strategies, and empathy in subjects suffering of ADHD to subjects with BPD, or comorbid ADHD and BPD. We also evaluated the relationship between ADHD symptoms and ED severity.N = 279 ADHD, N = 70 BPD and N = 60 BPD + ADHD) were recruited in a specialized centre for diagnosis and treatment of adults suffering from ADHD and BPD at the University Hospitals of Geneva.Four hundred and six French-speaking outpatients  [Borderline Symptoms Check-List (BSL-23) for BPD patients as previously described [Diagnostic Interview for ADHD in adults (DIVA 2.0), assessing DSM-IV ADHD criteria [Screening Interview for Axis II disorders (SCID-II), assessing DSM-IV BPD criteria [Patients underwent a clinical evaluation at their entry of the program conducted by trained psychiatrists to ascertain the diagnosis of BPD and/or ADHD according to DSM-5 criteria, and to exclude any organic condition and/or comorbid disorders that could better explain the symptoms. With the exception of ADHD and BPD (see below), other comorbidities were assessed clinically and using medical records only, and no structured interviews were used. In addition, subjects were administered the RS v1.1) ; and theescribed . Finallycriteria   when psychostimulants were not yet started.Beck Depression Inventory II (BDI-II) to assess the current level of depression as an indirect tool to evaluate the current distress associated with suffering from ADHD or BPD [All participants completed the D or BPD . BDI-II D or BPD , 38.The study was approved by the ethics committee of University Hospitals of Geneva and all subjects provided informed written consent.Several instruments were used to assess emotion regulation and reactivity, cognitive strategies to avoid worries, and empathy.Emotion Reactivity Scale (ERS) is a self-report questionnaire enquiring about emotional experience on regular basis. It consists of 21 items measuring emotion reactivity, based on three aspects: emotion sensitivity, intensity, and persistence. Each item is rated on a 5-point Likert scale from 0  to 4 (completely like me), with scores ranging from 0 to 40 for emotion sensitivity, 0 to 28 for emotion intensity and 0 to 16 for persistence, and total scores ranging from 0 to 84 [The  0 to 84 . The val 0 to 84 . The scaCognitive Emotional Regulation Questionnaire (CERQ) is a 36-item questionnaire consisting of 9 conceptually different subscales based on different cognitive emotion regulation strategies, overall divided in two main domains: the adaptive strategies domain entails putting into perspective, positive refocusing, positive reappraisal, acceptance, refocus on planning; the non-adaptive strategies domain comprises self-blame, other-blame, rumination, and catastrophizing. Each subscale contains 4 items referring to thoughts after the experience of a threatening or stressful life event. The items are measured on a 5-point Likert scale, ranging from 1  to 5  [The  always) . The val always) . Explora always) , 43.Basic Empathy Scale in Adults (BES-A) is a 20-item self-report scale, focusing on two components of empathy, the cognitive and affective components [The mponents , 45. Eacmponents . The scaADHD patients were compared to a sample of controls extracted from published data that have used the ERS, the CERQ or the BES-A , 47, 48.p < 0.05. Post-hoc tests were performed using pairwise comparisons (Bonferroni correction).All analyses were performed using Stata v14. Univariate comparisons between clinical groups were conducted using the chi square test for qualitative variables  or Fisher’s exact test when assumption of frequencies for chi square test were not met, and a one-way ANOVA for quantitative variables . T-tests were used to compare ADHD patients to a sample of controls extracted from published data that have used the ERS, the CERQ or the BES-A , 47, 48.Questionnaire scores were analyzed with linear regression models, using the diagnostic group as a fixed variable. Models were adjusted for age and gender in the event of significant differences between groups. For all continuous predictors, we examined the assumption of normality of the distribution of residuals with residual plots. These revealed no breach of model assumptions.Secondary analyses were performed to assess the relation between symptomatology and questionnaire scores. ASRS total score (for ADHD and ADHD+BPD groups) or BDI-II scores were added into the model as continuous predictors. Finally, association with ADHD presentation and current depressive episode were also assessed.F = 6.5; df = 2/405; p = 0.002). BPD and BPD + ADHD patients were more likely to be female than ADHD patients . ADHD patients were more likely to have a job than the two other groups  and to have 2 or more children  (Table ADHD patients were older than the other groups (N = 177/279) of ADHD patients did have at least one comorbidity compared to 97.14% (N = 68/70) of BPD patients and 93.33% (N = 56/60) of the ADHD+BPD patients. The use of medication differed among participants. A small fraction of patients took stimulants: 12.90% of ADHD patients, 11.66% of ADHD+BPD patients, and 1.43% of BPD patients. No patients took non-stimulant ADHD medication. The use of other medications  were lower in ADHD patients compared to the two other groups . BDI-II scores were higher in BPD and BPD + ADHD than in ADHD group  (Table Patients with ADHD were less likely to have other comorbid disorders than BPD and ADHD+BPD patients. Taken together 63.44% N = 77/279 ofp < 0.001)  Table . BPD andp < 0.001; Mean: 36.66 standard deviation (SD): 17.52 vs Mean: 47.38 SD: 18.39) [p < 0.001; Mean: 35.02 SD: 17.14) [Our ADHD patients displayed higher ED as measured by the ERS total score compared to a patient population recruited from community and local psychiatric clinics   , and lower scores for non-adaptive strategies  than BPD and ADHD+BPD patients respectively. BPD and BPD + ADHD groups did not differ. The subscale scores showed an overall similar pattern, with exception of the ‘acceptance’ (adaptive) and ‘rumination’ (non-adaptive) subscales where all three groups showed comparable scores. ADHD patients had similar scores as BPD patients in the ‘blaming others’ subscale but were still significantly lower than BPD + ADHD patients  [Our ADHD patients showed poorer cognitive emotions regulation strategies as indicated by higher levels of all non-adaptive cognitive strategies than 611 adults from the general population control group (o 14.19) . Resultsp < 0.05)  Table .p = 0.03 and 32.39 (SD = 3.86) vs 37.62 (SD = 3.46) t = − 10.52; p < 0.001 respectively) but higher affective empathy (39.81 (SD = 7.37) vs. 37.49 (SD = 3.39) t = 2.61; p = 0.01)  and all the subscales . This association was also true when looking at attentional or hyperactive/impulsive symptoms (data not shown).We constructed a model adding ASRSv1.1 score as predictor of the ERS, CERQ, and BES scores, analyzing only ADHD and ADHD+BPD patients. We found that the total ASRSv1.1 score was positively associated with ERS total score  and with each of the non-adaptive subscales (data not shown). It was not associated with adaptive strategies . The significant association was observed for attentional as well as hyperactive/impulsive symptoms.ASRSv1.1 total score was also significantly associated with higher score on non-adaptive cognitive strategies total score , affective subscale score , but not cognitive subscale score . The association was found only with attentional symptoms .ASRSv1.1 was significantly associated with higher empathy total score . The difference between ADHD and BPD patients was no longer significant , however the difference between ADHD and ADHD+BPD remained significant .When adding BDI-II total score as predictor in the model, we found that current level of distress was associated with higher ERS total scores  and higher non-adaptive strategies . When adjusting for BDI-II, the difference between groups was no longer significant .BDI-II total score was also predictive of lower CERQ adaptive strategies .BDI-II was not associated with empathy total score  than those with the attentional presentation. There were no differences between the two presentations of ADHD on CERQ adaptive and non-adaptive subscales  or on BES total score .ADHD patients with the combined presentation had higher ERS total scores  and ADHD+BPD patients . This was true for all the ERS subscales.Thirty-three (47.13%) BPD, 44 (15.77%) ADHD and 25 (41.67%) BPD + ADHD patients had a current major depressive episode. When adding current major depressive episode as a predictor in the model, we found that the difference between groups was still significant, with ADHD patients showing lower ERS total score than BPD patients  and with higher CERQ non-adaptive strategies . When adjusting for current major depressive episode, ADHD patients still showed higher CERQ adaptive strategies than BPD patients , but the difference with ADHD+BPD was no longer significant . Furthermore, with this adjustment the difference between ADHD and BPD for CERQ non-adaptive strategies was no longer significant , but was still significant for the comparison between ADHD and ADHD+BPD  with lower scores in ADHD patients.Current major depressive episode was also associated with lower CERQ adaptive strategies .No association between BDI-II and empathy total score was found .We found that patients with ADHD had higher scores on scales measuring ED compared to community-based psychiatric patients. Previous studies suggested a slow return to emotional baseline, increased intensity and instability of negative emotions, and a predominance of negative emotions in ADHD , 4, 10. We found that ADHD as well as BPD displayed lower empathy than a sample of adolescents not suffering from psychiatric disorder. This slight deficit in empathy might contribute, as in BPD, to poorer emotion regulation capacities , 55. PreConcerning ADHD presentation, the combined type was associated with higher emotional reactivity as previously shown in other studies , 4. No dThe hypothesis of a possible additive effect of both disorders (ADHD + BPD) was not really corroborated by our results since no significant difference between BPD and ADHD+BDP was found. However, this observation might be due to a ceiling effect as the scales we used were designed to assess emotions present in the general population and not in highly emotionally dysregulated samples such as BPD.Part of the differences between groups was related to the level of distress as measured by the BDI-II . Our resOur study has several limitations. Firstly, we did not have a control population and used BPD patients as a comparison to assess ED, cognitive emotion regulation strategies and empathy in ADHD patients. We nevertheless compared our patients to samples from the general population, although not matched on age and gender, derived from others studies and we are quite confident that the assessment of emotion components in our study is a true reflection of the difficulties endured by patients suffering from BPD and ADHD. Secondarily, there was a substantial difference in the size of our three groups, the ADHD group being the largest one. This may have slightly biased our results keeping in mind that ED in ADHD subjects was the main focus of our study, the two other groups being here considered as comparison samples. Thirdly we used self-report measures to assess the different aspects of emotions. However it has previously been shown that self-report measures are reliable if used in combination with interviews, which was the case in our study . FinallyOur findings support the importance of exploring facets of ED as well as cognitive emotion regulation strategies in ADHD. These may, as in BPD, be an important feature of the disorder and be associated with its severity. In general, our results showed that maladaptive cognitive emotion strategies for management of emotion, such as ‘self-blame’, ‘catastrophizing’, ‘other-blame’ and ‘rumination’ are often used by ADHD patients and should be considered in individual and group psychotherapeutic approaches offered to these patients. Furthermore, ADHD patients showed a better use of cognitive emotional regulation strategies and less emotional reactivity than BPD patients. Similarities were also found between these populations, such as the tendency of using ‘blaming others’ and the absence of a difference in cognitive empathy, possibly suggesting a common deficit in perception of self and others in emotionally triggering situations.


Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear. Towards this end, 100 children with ADHD and 140 normal controls were obtained from the ADHD-200 Consortium. The raw features were derived from the temporal variability between intrinsic connectivity networks (ICNs) as well as the demographic and covariate variables. The diagnostic model was based on the support vector machines (SVMs). The performance of diagnostic model was analyzed using leave-one-out cross-validation (LOOCV) and 10-folds cross-validations (CVs). The diagnostic model based on inter-ICN variability outperformed that based on inter-ICN functional connectivity and inter-ICN phase synchrony. The LOOCV achieved total accuracy of 78.75%, the sensitivity of 76%, and the specificity of 80.71%. The 10-folds CVs achieved average prediction accuracy of 75.54% ± 1.34%, average sensitivity of 70.5% ± 2.34%, and average specificity of 77.44% ± 1.47%. In addition, the discriminative patterns for ADHD were discovered using SVMs. The discriminative patterns confirmed with previous findings. In summary, individuals with ADHD could be identified through inter-ICN variability, which could be potential biomarkers for diagnostic model of ADHD. According to epidemiological survey, ADHD affects nearly 5–10% of children and 4% of adults2. Patients with ADHD always exhibited problematic behaviors , academic failure, social dysfunction in their daily life3. Therefore, accurate diagnosis of ADHD is beneficial for individuals, as well as their related family and society. Clinical diagnostic models for ADHD were based on behavioral scales, which was subjective during implementation. The structural and functional MRI-based evidences suggested that the brain of ADHD might wire differently form healthy controls7, thus neuroimaging features might be potential biomarkers for diagnostic model of ADHD. Several neuroimaging-based diagnostic models have been established for ADHD using machine learning18. The diagnostic models could be beneficial for ADHD in twofold: classification of patients and discovery of ADHD-related discriminative patterns. However, it is a challenging task to classify individuals with ADHD from healthy controls based on brain imaging features. One challenge is the significant high dimensions of features in the diagnostic models. Another challenge is extracting novel features that could discriminate ADHD. On the one hand, the high dimensional features could increase the complexity of diagnostic models. On the other hand, novel features with low dimensions could be beneficial for diagnostic models of ADHD. So far, the dynamic functional connectivity of ADHD remain unexplored.Attention deficit hyperactivity disorder (ADHD) is a common disorder that spreads in school-age children20. There are about ten to twenty well-established ICNs found by independent component analysis (ICA)23. The ICNs were related to specific brain functions and even correlated to behavioral symptoms24. The ICNs were consistent across different individuals25. Thus, the ICNs might be potential biomarkers for brain disorders. The ICN contains two kinds of features: spatial patterns and temporal patterns. The spatial maps of ICNs were reliable across resting state sessions based on ICA and dual regression26. The complexity of temporal patterns for ICNs exhibited moderate-to-high test-retest reliability under different scan conditions27. However, the spatial patterns of ICN included tens of thousands of features, which could increase the complexity of the diagnostic models. Notably, the temporal patterns of ICNs included appropriate number of features, which could reflect the network-wise brain dynamics. There are two types of ICN-based temporal patterns: univariate features within ICN and bivariate features between ICNs. Altered intra-ICN amplitude of low frequency fluctuations and inter-ICN functional connectivity were found in children with ADHD28. Moreover, the intra-ICN entropy and inter-ICN synchrony might predict the clinical symptoms for ADHD29. However, the dynamic inter-ICN functional connectivity for ADHD remains largely unexplored.Intrinsic connectivity networks (ICN) are spatially independent resting state networks, which are intrinsically dynamic and anti-correlated spontaneous brain systems30. Previous evidence found that there were several dynamical states of functional connectivity between ICNs31. Altered dynamic functional connectivity patterns were found between eyes-open and eyes-closed conditions32. Moreover, the dynamic functional connectivity might underlie spontaneous fluctuations in attention33. Notably, the dynamic functional connectivity could successfully discriminant patients with ADHD34. However, those studies focused on the temporal clusters of dynamic functional connectivity. Current evidence found that the strength of functional connectivity showed significant fluctuations over time35. Given concerns about the temporal variations in time-resolved ICNs, the temporal variability of dynamic functional connectivity still remains unclear.Dynamic functional connectivity reflected the time-varying properties of brain dynamics. The ICNs exhibited dynamic functional connectivity in healthy subjectsIn this paper, we aimed to build diagnostic model for ADHD based on temporal variability of dynamic functional connectivity. We also sought to find the discriminative patterns of dynamic functional connectivity for ADHD. To achieve these goals, a cohort of children with ADHD and a cohort of healthy controls were obtained from the ADHD-200 Consortium. The diagnostic model was based on SVMs and inter-ICN variability. The performances of diagnostic model and the most discriminative patterns were determined by leave-one-out cross-validation (LOOCV) and 10-folds CVs, respectively. The prediction accuracy of different inter-ICN features were also analyzed for comparison additionally.The diagnostic model exhibited moderate performance based on LOOCV. Table The diagnostic model exhibited well-established performance based on 10-folds CVs with 1000 times of simulations. Figure Figure Figure Table Moreover, the diagnostic model is simulated 1000 times using 10-folds CVs. Considering the top 10 most discriminative features, the gender parameter appears 862 times. The verbal IQ parameter appears 143 times. The performance IQ parameter appears 1000 times. The full IQ parameter appears 1000 times. In addition, the head motion parameter appears 994 times during 1000 simulations of 10-folds CV.This paper proposed novel inter-ICN variability to identify individuals with ADHD. To the best of our knowledge, this is the first study that investigated the inter-ICN variability for ADHD. The inter-ICN variability was based on the variance of phase differences. The performance of diagnostic model was based on LOOCV and 10-folds CVs. Specially, the 10-folds CVs was simulated 1000 times to reduce overfitting problems. The well-validated results suggested that the diagnostic model could identify patients with ADHD from normal controls. The discriminative patterns of inter-ICN variability was found using LOOCV and 10-folds CVs. In addition, the IQ and head motion parameters were related to ADHD. Together, ADHD could be discriminated from healthy subjects through the inter-ICN variability as well as demographic and covariate variables. The discriminative patterns of inter-ICN variability could be potential biomarkers for ADHD.9. ADHD-200 Consortium provided a significant large number of samples of ADHD and normal controls10. Based on ADHD-200 samples, different kinds of features and machine learning methods were applied to diagnosis ADHD13. Using ADHD-200 samples, the best performance of classification for ADHD is 69.59% based on voxel-wise network features17. Total accuracy of 55% was achieved using multimodal features12. The prediction accuracy is 67% for combined type and inattentive type of ADHD classification based on combination of functional features36. However, most of the current diagnostic models for ADHD exhibited poor performance with either low sensitivity or low specificity. One possible explanation of low prediction accuracy was that the dimensions of neuroimaging features were relatively high in most diagnostic models. The regional cortical surface-based morphological patterns could discriminate ADHD patients from healthy controls based on machine learning methods15. However, surface-based or voxel-based morphological patterns were structural measures, which could not reflect the brain function or topology of ADHD. Notably, the diagnostic model based on hyper-connectivity could classify ADHD with total accuracy of 82.9%, sensitivity of 83.9% and specificity of 81.6%18. However, the performance of classifiers was not validated using sufficient simulations of 10-folds CVs. Moreover, the mathematical implementation of the aforementioned study was more complex than conventional methods. With novel inter-ICN variability, this study could identify patients with ADHD based on LOOCV and 10-folds CVs. Using LOOCV, the total accuracy was 78.75%, the sensitivity was 76%, and specificity was 80.71%. Using 10-folds CVs, the average prediction accuracy was 75.54% ± 1.34%, the average sensitivity was 70.5% ± 2.34%, and the average specificity was 77.44% ± 1.47%. The diagnostic model exhibited balanced accuracies of sensitivity and specificity. Therefore, the diagnostic model could be beneficial for clinical applications. In addition, we proposed a novel neural metrics with relatively low dimensions of features, which could reduce the complexity of the diagnostic models. Specially, the results were based on 1000 times 10-folds CV, while most of the previous methods were based on LOOCV or one run of 10-folds CV. In summary, our method could open a new perspective for diagnosing ADHD.A number of diagnostic models have been built for ADHD based on resting state fMRI. Using a relatively small number of subjects, patients with ADHD could be discriminated from healthy controls based on feature extraction and support vector machines (SVMs)4. Altered brain topologies were found in ADHD based on graphical measures37. Of note, our previous study also found altered brain topologies based on inter-ICN functional connectivity28. In this study, the diagnostic model identified certain features of variability between ICNs  that exhibited discriminative powers. The DMN-related cortex exhibited altered resting state regional homogeneity9. Enhanced DMN-related activities were correlated to sustained attention deficits38. The RFPN-related variability might be related to the prefrontal-striatal-cerebellar circuit in ADHD39. In addition, the visual-related ICNs  were found with discriminative powers. The ICN-based evidences noted that the sensory- and visual-related brain networks might contribute to ADHD28. Previous evidence also found enhanced brain activities in sensory-related brain regions4. In summary, the informative discriminative patterns could be beneficial to diagnostic model of ADHD.Discriminative patterns of inter-ICN variability were found for ADHD. In the discriminative model, the positive weights indicated the increased inter-ICN variability in ADHD, while the negative weights indicated the decreased inter-ICN variability in ADHD. Most of the discriminative patterns were with positive weights, which suggested increased inter-network activity in ADHD. Previous evidence found that there were enhanced brain activities in ADHD than normal controls31. Moreover, the dynamic functional connectivity between ICNs were related to visual attention 32. However, the aforementioned studies were based on temporal clustering, which could not reflect the temporal fluctuations of dynamic functional connectivity. A previous study applied amplitude of low frequency fluctuations (ALFF) on temporal dynamic functional connectivity to predict brain maturity40. However, the results were based on sliding-window method, which has certain limitations . Given current concerns on temporal variability of dynamic functional connectivity, the proposed inter-ICN variability was based on the phase differences, which did not depend on sliding-window. Thus, the inter-ICN variability might be potential biomarkers for human connectome.The temporal variability of dynamic functional connectivity exhibited better discriminative power than conventional measures of connectivity. A possible explanation was that the inter-ICN variability might reflect the dynamic properties of brain networks. The inter-ICN functional connectivity and phase synchrony was static measures, which contained less information of brain dynamics than inter-ICN variability. Previous evidence suggested that the brain should exhibited dynamic activities during resting state. Seven temporal clusters of dynamic functional connectivity were found based on sliding-window and machine learning methods41. The IQ scores also correlated to ALFFs within ICNs, implying that the IQ might contribute to ADHD28. A recent study found that functional connectivity between ICNs could predict the IQ scores for ADHD42. In this paper, the IQ scores exhibited discriminative power for ADHD, suggesting that the IQ might play an important role in ADHD. Without the IQ scores, the diagnostic model achieved total accuracy of 71.67% based on LOOCV, which was much lower than that with the IQ scores. The personal characteristic features  could outperform neuroimaging data in diagnostic models for ADHD43. Moreover, the IQ scores were negatively related to ADHD in our diagnostic model. The results implied that the ADHD group exhibited lower IQ scores than healthy group.Previous evidences found that the IQ was related to ADHD. The developments of cortical thickness and white matters might be delayed in ADHD with low IQ45, since the functional signals were corrupted with severe head motions. The original sample size for this study was 245 subjects. Only 5 subjects exhibited severe head motion, resulting in 240 subjects for diagnostic model. Thus, the preprocessed subjects could represent the original samples. Besides, we used FD as a covariate in the diagnostic models. Previous studies suggested that head motion should be considered as a covariate variable in statistical models45. Moreover, head motion might affect the test-retest reliability of resting state features46. For group-wise analysis, the mean FD was computed as head motion parameter48. In this study, head motion significantly improved the performance of diagnostic model. The results suggested that head motion should be carefully considered in ADHD research.Given current concerns on head motion in resting state fMRI, the head motion parameter was taken as supplementary feature in the diagnostic model. The power weight of head motion was 1.63, which means that patients exhibited more head motion than normal controls. Without head motion parameter, the diagnostic model achieved total accuracy of 74.17% using LOOCV, which was lower than that with head motion. Thus, ADHD might possess distinctive head motion during resting state. The head motion has been identified as an important confound in resting state fMRI. Previous studies suggested that the subjects with Frame-Displacement (FD) more than 0.5 mm should be discardedOne advantage of this study was applying inter-ICN variability to diagnosis ADHD. The feature dimensions of inter-ICN variability was much lower than regional- or voxel-wise measures. Moreover, the inter-ICN variability outperformed conventional connectivity estimators in the diagnostic model. Thus, the inter-ICN variability could be beneficial for classification of ADHD. Another advantage of this study was applying 1000 simulations of 10-folds CVs on the diagnostic models. The results were well-validated compared to previous studies. Moreover, the prediction accuracy was balanced with equal sensitivity and specificity. The third advantage of this study was applying SVMs to discover the discriminative patterns for ADHD. The linear SVMs found certain discriminative features, which could be potential biomarkers for ADHD. The fourth advantage of this study was applying the IQ scores as the predictors in the diagnostic models. The IQ scores were related to ADHD, and could significantly improve the performances of the diagnostic models. The fifth advantage of this study was applying head motion as a predictor in the diagnostic models. The head motion parameter was found to be associated with ADHD. The diagnostic models exhibited better performances with head motion parameter. Overall, the inter-ICN variability, IQ scores and head motion might contribute to diagnosis of ADHD. Moreover, the diagnostic models with balanced performances was well-validated using 1000 simulations of 10-folds CVs.There were several limitations which should be noted in this study. One limitation was that the neuroimaging datasets were obtained using different scan parameters. There were more than three different scan protocols for this dataset. Due to the limited information for this public datasets, we introduced the scan difference  as a covariate. However, the performance of diagnostic model with scan differences was a little lower than that without scan differences. The diagnostic model with scan differences exhibited accuracy of 76.67%, sensitivity of 72%, and specificity of 80%. One possible explanation was that there were five different scan protocols for anatomical images in Peking_3. Thus, the covariate of scan difference might be mislabeled in the diagnostic models. The effects of scan parameters should be taken into account in future study. Another limitation was that the diagnostic model was based on Chinese children in order to remove the effects of populations and versions of clinical scales. We tested our diagnostic model on datasets from the NYU site. However, the diagnostic model only achieved total accuracy of 54.08%. We also applied our method on the NYU children using LOOCV, which achieved total accuracy of 58.67%. One explanation was the training and testing models were based on different populations. Another explanation was the different scan parameters of the two datasets. The third explanation was the two datasets were based on different versions of clinical scales. Therefore, we should test our model on an independent dataset of Chinese children in future study. The parameters of classifiers should be optimized to improve the performance of the diagnostic model. The third limitation was not considering the subtypes in the diagnostic model, since the subtypes contained the label information. The diagnostic model for combined type of ADHD and normal controls exhibited total accuracy of 69.44%, sensitivity of 73.0% and specificity of 65.7%. The diagnostic model for inattentive type of ADHD and normal controls exhibited total accuracy of 77.3%, sensitivity of 77.4% and specificity of 77.1%. The fourth limitation was based on single modal of neuroimaging features. The performance of the diagnostic model could be improved with multi-modal imaging methods . The fifth limitation was not considering the eyes-open/closed conditions in the diagnostic model. The eyes-open/closed conditions could affect functional connectivity. The subjects in this study were asked to have their eyes opened or closed. However, we cannot control this confounding variable due to limited information provided by ADHD-200 website. Additionally, the inter-ICN biomarkers should be validated using an independent dataset for clinical approach. Thus, we plan to test the diagnostic model using different features and populations in further study. We also plan to obtain multimodal imaging datasets in further study. Moreover, the performance of the diagnostic model should be improved using different machine learning methods .This paper investigated the temporal variability of dynamic functional connectivity to diagnosis children with ADHD based on machine learning methods. The diagnostic models could discriminate patients with ADHD using cross-validations. Moreover, the discriminative patterns of inter-ICN variability were discovered by the diagnostic model. In summary, individuals with ADHD could be identified by machine learning based on the inter-ICN variability, which could be potential biomarkers for ADHD.The ADHD-200 Consortium provided a great number of individuals with ADHD and normal controls (http://fcon_1000.projects.nitrc.org/indi/adhd200/). ADHD-200 Consortium were consisted of datasets from eight sites . To remove the effects of sites, the participants in this study were based on datasets from the Peking site. There were 245 participants using datasets combined from Peking_1, Peking_2, Peking_3 and Peking_1_test. The demographic and covariate information could be found in Table Both anatomical MRI and resting state fMRI were scanned for each subject. The neuroimaging datasets were obtained from a SIEMENS TRIOTIM syngo 3-T MRI scanner. The anatomical MRI data was a high-resolution T1-weighted MPRAGE 3D volume, which was defaced to protect patient identity. The parameters of the anatomical MRI could be found in Table The anatomical and functional MRI datasets were preprocessed using scripts from 1000 functional connectome project. The preprocessing scripts were based on AFNI and FSL. The anatomical MRI datasets were skull-stripped, segmented into three kinds of brain tissues , nonlinearly deformed into standard MNI brain space. After discarding the first five volume, the resting state fMRI datasets were preprocessed using the following steps: slice-timing correction, motion correction, skull stripping, regressing out nuisance signals  as well as linear and quantic trends, nonlinearly wrapped into standard brain space, bandpass filtering (0.01–0.1 Hz), spatially smoothing (FWHM = 6 mm). Of note, after motion correction, the mean frame-wise displacement (FD) was computed as the motion covariate for each subject. Five participants with mean FD larger than 0.5 mm were discarded, resulting in 240 subjects for diagnostic model. According to statistics, 37 subjects (37%) were combined type of ADHD. 1 subject (1%) was hyperactive/impulsive type of ADHD. 62 subjects (62%) were inattentive type of ADHD.49.In this study, ten well-established ICNs were taken as spatial templates. The template ICNs were obtained using meta-analysis of the BrainMap database. The names and abbreviations of the ten ICNs were present in Table The inter-ICN variability was based on Hilbert transform with the following procedures: 1) obtain pair-wise time-courses of ICNs; 2) apply Hilbert transform on the two time-courses; 3) obtain the instantaneous phases of each time-course; 4) compute the instantaneous phase differences between the two time-courses; 5) transform instantaneous phase differences into –pi to pi; 6) the compute the variance of the instantaneous phase differences. Finally, after looping through the ten ICNs, a vector with 10 × 9/2 = 45 features were obtained for each subject. In addition, the conventional functional connectivity (FC) and phase synchrony (PS) were computed for comparisons.The original features were consisted of inter-ICN variability, IQ scores, age, sex and head motion. The diagnostic model could be denoted as the following formula:VARi denotes the ith inter-ICN variability, and wi1 denotes the weight of the ith inter-ICN variability. The IQi denotes verbal IQ, performance IQ or full IQ, and wi2 denotes the weight the ith IQi. The sex denotes the gender of subject, and w3 denotes the weight of sex. The age denoted the biological age of subject, and w4 denotes the weight of age. The mFD denotes mean frame-wise displacement, and w5 denotes the weight of head motion.In the diagnostic model, the label denotes the diagnostic information of each subject . The 50. SVM was designed for classification of two classes. The advantage of SVM was solving small sample problem, nonlinear problem and high dimensional pattern recognition. The basic idea of SVM was searching an optimized hyperplane which can classify different kinds of samples. In this paper, sequential minimal optimization (SMO) was applied on training and testing datasets to search the optimized hyperplane. SMO was an iterative algorithm with fast speed to effectively solve the optimizing problems of SVMs51. The trick of SMO was analytically solving a set of smallest possible sub-problems instead of the original SVMs. SMO significantly improve the performance and computation times of SVMs. Moreover, SMO exhibits good performance for linear SVMs. Here, SMO with linear kernels was applied in the diagnostic model. The implementation of SMO procedure was based on WEKA, which is a popular machine learning software (www.cs.waikato.ac.nz/ml/weka). In addition, the weights of features were considered as their contributions to the diagnostic model52. The positive weight means increased inter-ICN variability in ADHD, while the negative weight means decreased inter-ICN variability in ADHD.The diagnostic model was analyzed using support vector machines (SVMs), which was proposed by Cortes and Vapnik in 1995To evaluate the performance of diagnostic model, cross-validation (CV) was applied in this study. In an n-fold CV, the original samples were first divided into n-folds. Then, one-fold of samples were selected as testing samples, leaving the rest instances as training samples. Third, a diagnostic model was built on the training samples. Fourth, the trained diagnostic model was tested using testing samples. Finally, n diagnostic models were trained and tested based on n-fold CV. In this paper, leave-one-out cross-validation (LOOCV) and 10-folds CV were applied to evaluate the performance of diagnostic model. Specially, 1000 simulations of 10-fold CV were performed to validate the effects of random seeds in partitions of folds.The performance of classification was evaluated by total accuracy (Acc.), sensitivity (Sen.) and specificity (Spec.). Here, let TP denotes the number of children with ADHD correctly classified as patients. FP denotes the number of healthy controls incorrectly classified as ADHD. TN denotes the number of correctly identified healthy subjects. FN denotes the number of incorrectly identified patients. Total accuracy is defined as the proportion of correctly predicted instances /(TN + FN + TP + FP)). Sensitivity is the proportion of correctly classified positive instances ). Specificity is the proportion of correctly classified negative instances ).The MRI datasets could be obtained from a public database (http://fcon_1000.projects.nitrc.org/indi/adhd200/).


Local landraces constitute a valuable genetic pool of increased diversity that can be exploited in breeding programs for the production of new commercial cultivars with targeted traits. Despite the acknowledged importance of local landraces, recent market trends and modern horticultural production systems have shifted farmers and related sectors to cultivation of commercial cultivars and hybrids due to their high yield and uniformity of the final product. However, this trend results only to short-term benefits while it puts in danger the valuable genetic resources of local horticultural landraces which may be proven useful under a global climate change. Moreover, consumers' concerns about food quality, its origin and production process as well as geographical indication (GI) of products are in increasing demand. Especially for the EU, Protected Designation of Origin (POG), Protected Geographical Indication (PGI) and Traditional Specialties Guaranteed (TSG) labels have been established, while according to the TRIPS  agreement which is applicable to the WTO (World Trade Organization) participating countries other labels such as Certification Mark and Collective Mark are also available for non-EU countries. Within this context and considering the special features that characterize local landraces and the cultural heritage and connection with specific regions, it is of pivotal importance to further investigate and point out these features that could result in high added value products and the reinforcement of rural economies.Therefore, the prospect of valorization of landraces and traditional crop varieties seems very promising toward a sustainable and quality-orientated horticulture.Major et al.  tried to differentiate 13 shallot landraces preserved along the Croatian coast by using morphological characters, while the authors also studied the chemical composition of the tested landraces. After using a multivariate classification, it was suggested that Croatian shallot landraces can be classified into three distinct groups , while biochemical profiles and morphological parameters could be used for species identification. “Calçots” are a typical Spanish product which is produced by immature floral stems of onion landrace “Blanca Tardana de Lleida” resprouts. In the study of Sans et al., the effect of pre-harvest factors (genotype and growing conditions) on chemical composition and sensorial properties of calçots was evaluated by assessing the original genotype and three new genotypes derived from the original one under different conditions. Based on the results, modulation of cultivation practices, growing conditions, and breeding selection may increase yield and quality of the final product. Another landrace that is described in this e-book is “Carota di Polignano,” a multi-colored Italian landrace of carrot which was evaluated for the possibility of biofortifying with iodine by Signore et al. under open field and greenhouse conditions. The results of that study demonstrated that open field cultivation allows for better iodine enrichment of the final product, thus Recommended Daily Allowance may be achieved easier through the consumption of lower amounts of these carrots.This e-book aims to highlight the significance of conserving local landraces toward shaping future horticultural production in a sustainable framework and point out all these special features that characterize local landraces and make them valuable, especially under harsh conditions and/or abiotic and biotic stress factors. In this context, Massaretto et al. evaluated the potential of using two tomato landraces , endemic of the Spanish Southeast area which is characterized by harsh climatic conditions, as alternatives to commercial varieties for cultivation under saline growing conditions. High adaptability of local landraces to salinity was observed, since both landraces exhibited high fruit quality and yield comparing to the commercial cultivar tested under the same conditions. Similarly, Figás et al. evaluated 12 long shelf-life tomato genotypes, including seven landraces, for suitability under open field and protected environment growing conditions. The variation in chemical composition, plant morphology, and agronomic performance revealed that growing conditions have a high impact on shelf-life of tomato fruit. In that way, landraces could be a useful material in breeding programs focusing on adaptation of tomato genotypes to greenhouse cultivation.Tomato is the most important fruit vegetable and many local landraces are preserved throughout the world as local or farmer varieties. In their study,  Casals Missio et al. evaluated agronomic and quality features as well genetic variability of 32 lettuce genotypes, including landraces and modern varieties, in organic farming agrosystems. Moreover, farmers and consumers participated in genotype characterization in terms of yield, susceptibility to Bremia lactucae infections, visual appearance, and taste. According to the results, modern lettuce varieties and landraces were clearly distinguished, mostly due to significant differences in marketable weight and tolerance to infestations by B. lactucae, while farmers and consumers showed high capacity for phenotype characterization and they should be included in future research projects for local landraces recovery. Moreover, Renna et al. performed studies of nutritional value, mineral composition and antioxidant activity evaluation, as well as ethnobotanical surveys of various unconventional vegetables traditionally used in Puglia . The studied vegetables included offshoots of two globe artichoke landraces, greens of summer squash and faba beans landraces, and crenate broomrape. Two more research articles and two mini reviews highlighted the importance of genetic diversity of legume germplasm. In particular, Cullis et al.  presented the features that allow marama bean, an orphan legume indigenous to Southern Africa , to survive under harsh conditions. It was also suggested the importance of unveiling the acclimatization mechanisms to improve the performance of major crops under a climate change regime. The next mini review authored by De Ron et al. addressed the importance of conservation of common bean, runner bean, and cowpea germplasms for the genetic improvement of varieties toward the sustainable production of legumes. The recovery of “Caparrona” or “Caparrona de Monzón” common bean landrace for commercial purposes concerned Mallor et al. who performed both in situ and ex situ experiments after collection of seed samples of the aforementioned landrace. From all the collected seed samples, only two of them were finally used to register and commercialize “Caparrona” beans as a gourmet product by a local producers' association. In the next original research article authored by Rivera et al., a Spanish core collection of 202 common bean landraces was evaluated in terms of chemical composition, showing a high genetic diversity. The results also showed no significant correlation between chemical composition and sensorial quality which could be further valorized in the development of elite cultivars with superior nutritional composition and sensory traits. Finally, Marconi et al. performed a genetic characterization of 175 apple accessions from Central Italy, including local, modern, and ancient varieties, by using 19 Single Sequence Repeats (SSR) markers. The results showed a significant genetic variation among the tested accessions, while duplicates, synonyms, and homonyms were also identified.On the other hand, the interest in leafy vegetable landraces is also discussed in the present e-book. Modern horticulture faces serious challenges due to climate change and the increasing market demands for food quality and food safety. Local landraces could be proven a useful tool toward shaping a sustainable and quality-oriented horticulture. We believe that the present e-book will raise the scientific interest to local landraces of horticultural species in order to reveal their importance to crop adaptation to the ongoing climate change, as well as their socio-economic impact on rural communities. We also believe that future research concerning local landraces of horticultural species is of pivotal importance in order to unveil their special features, to evaluate these genotypes under intensified cultivation systems and to include them in breeding programs for the production of new elite genotypes.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


NANOG expression, and the acquisition of stem-like cellular features. As validated in a large (n = 123) CRC cohort, IL-6 secretion was invariably accompanied by increased FRA1 deacetylation at K116 and an overall increase in its protein levels, coincident with malignant progression and poor prognosis. Of note, combined treatment with the conventional cytotoxic drug 5-FU together with Tubastatin A, a HDAC6-specific inhibitor, resulted in a significant in vivo synergistic inhibitory effect on tumor growth through suppression of CRC stemness. Our results reveal a novel transcriptional and posttranslational regulatory cross-talk between inflammation and stemness signaling pathways that underlie self-renewal and maintenance of CRC stem cells and promote their malignant behavior. Combinatorial treatment aimed at the core regulatory mechanisms downstream of IL-6 may offer a novel promising approach for CRC treatment.Colorectal cancer (CRC) has long been known for its tight association with chronic inflammation, thought to play a key role in tumor onset and malignant progression through the modulation of cancer stemness. However, the underlying molecular and cellular mechanisms are still largely elusive. Here we show that the IL-6/STAT3 inflammatory signaling axis induces the deacetylation of FRA1 at the Lys-116 residue located within its DNA-binding domain. The HDAC6 deacetylase underlies this key modification leading to the increase of FRA1 transcriptional activity, the subsequent transactivation of  HoweverConventional cytotoxic cancer treatment is only partly effective because it mainly targets the bulk of cancer cells without affecting the stem-like cancer cells capable of tumor initiation, self-renewal, differentiation, and chemoresistance. The latter highlights the need to circumvent therapy resistance by specifically targeting the stem-like cellular component of malignancies. Thus far, CD44, CD133, and CD166 were shown to represent informative CSC markers in colon cancer . MoreoveFOSL1 gene and an important transcriptional downstream target of the IL-6/STAT3 signaling axis leading to CRC aggressiveness through EMT induction [FOSL1 (FRA1) expression level was reported to result from the activation of the IL-6/STAT3 [FRA1 is a member of the FOS family of TFs encoded by the nduction , motilitnduction , cancer nduction , and stenduction , 18. Ele-6/STAT3 , 15, and-6/STAT3 , 19 pathAcetylation is a well-known regulatory posttranslational modification. In particular, acetylation at specific residues of several TFs has been shown to represent an important regulatory mechanism. Notably, lysine acetylation is not only restricted to histones but is also found in numerous TFs, including p53, nuclear factor (NF)-κB, and STAT3 . MechaniIn the present study, we investigated posttranslational regulatory mechanisms downstream of the IL-6/STAT3/FRA1 inflammatory signaling axis that mediate colon cancer stemness and malignancy and explored novel combinatorial therapeutic approaches to target CRC stem and bulk cells.FOSL1 expression. Next, we determined the effects exerted by recombinant IL-6 on both cell lines. Phenotypic analyses by in vitro cell migration, invasion, sphere formation, and chemo-resistance assays and by in vivo lung metastasis assay in nude mice consistently indicated that IL-6 exposure promoted CRC stemness and malignancy  group, and IL-6 stimulation enhanced this effect in EV group  in DLD1 and HT-29 cells using a lentivirus-based approach  and Cisplatin in DLD1 and HT-29 cells . As for the residue116, the difference was mainly contributed from the polar energy terms   Figure .Fig. 2InMutagenesis and luciferase reporter assays provided experimental confirmation of the above results: the FRA1-K116R mutant that mimics deacetylation at the Lys-116 residue was characterized by an increment of FRA1 reporter luciferase activity, whereas the acetylation mimic FRA1-K116Q/E/A mutants resulted in a reduction of FRA1-luc activity. Notably, the FRA1-K116R/K121R double mutant had similar effects when compared with the single FRA1-K116R mutant, while the K121R mutation alone did not affect FRA1 reporter activity. Besides, both the Δ3 deletion mutant  and the dimerization-defective (L154A/L161A) mutant of FRA1  did not To enable the analysis of K116 deacetylation in endogenous FRA1, we generated a polyclonal antibody specifically recognizing the acetylated Lys-116 residue. The specificity of the anti-K116Ac antibody was validated by western blot and IP analyses Figure  and S4d.FOSL1 transcription and thereby increases CRC stemness as shown above, we asked whether FRA1 K116 deacetylation could be directly involved in the modulation of CSCs properties. As shown in Fig. FOSL1 transcription, though with a decrease in the relative level of K116-acetylated FRA1 versus total FRA1 in both nucleus and cytoplasm  and NANOG mRNA level in one CRC patient cohort from the GSE24551-GPL5175 [Clinical validation of these results was provided by the positive correlation between -GPL5175  dataset NANOG is directly regulated by FRA1, we first analyzed in silico its promoter sequence using the JASPAR database (http://jaspar.genereg.net/) and predicted a FRA1/AP-1-binding site (FBE) 45–56 nucleotides upstream of the translational start site. Chromatin IP–quantitativePCR analysis confirmed that in DLD1 cells both wild-type FRA1 and FRA1-K116R, though not FRA1-K116Q, bind to the NANOG gene promoter through the FBE site   Figure . On its ion Fig. . Similarays Fig. .NANOG in DLD1 and HT-29 cells using a lentivirus-based shRNA approach  was employed. Immunofluorescence analysis showed increased expression and nuclear co-localization of FRA1, phosphorylated STAT3, and NANOG protein in the cancer samples when compared with adjacent (para-carcinoma) normal tissues  counterparts  and predicted that only NANOG, SOX2, and LGR5 encompass AP-1-binding sequences within their promoters, thus suggesting that they may represent downstream targets of the IL-6/STAT3/FRA1 signaling axis. As shown here, only NANOG does factually represent a direct FRA1 target gene. The physical interaction between FRA1 and NANOG occurs through a FRA1/AP-1-binding site (FBE) upstream of the translational start site. FRA1 deacetylation at Lys-116 enhances this interaction and further activates NANOG transcription.Among the genes known to regulate CSC properties, we used JASPAR database (NANOG expression through STAT3 phosphorylation and that pSTAT3 (Y705) bound to the NANOG promoter drives tumor onset and self-renewal in the liver [NANOG expression in CRC is regulated by extracellular insulin-like growth factor signaling via STAT3 phosphorylation [FOSL1 and NANOG expression in a STAT3-dependent fashion. Of interest, FOSL1 gene knockout significantly blocked IL-6-induced NANOG expression, while NANOG overexpression in FOSL1 knockdown cells partially rescued the negative effects of FOSL1 ablation on chemo-resistance, self-renewal, and tumorigenesis. Notably, NANOG knockdown significantly inhibited IL-6 induced in vitro ability of migration, invasion, chemo-resistance, and self-renew and in vivo ability of tumorigenesis. Hence, NANOG represents, presumably together with others [NANOG promoter and play an essential role in sustaining self-renewal [n = 123) cohort of prospectively collected human colorectal carcinomas. We found that, while FRA1 was mainly localized in the nucleus of cancer cells, its acetylated form was mainly present in the cytoplasm, i.e., indicative of low K116Ac levels in its nuclear fraction. More importantly, high FRA1 protein expression and its relatively low K116 acetylation levels in the nucleus highly correlate with IL-6 secretion and with poor prognosis and overall survival among CRC patients. In summary, the results of western blots and IHC both showed that IL-6 deacetylates FRA1-K116 in the CRC cells.It has been reported that CD24 regulates he liver . Moreoverylation . Severalrylation –12. Our h others , a bona -renewal . We showThe elucidation of the cellular and molecular mechanisms underlying the enhancement of colon cancer stemness downstream of the IL-6/STAT3/FRA1 signaling axis opens novel opportunities to develop alternative and possibly more effective therapeutic strategies. Small molecules inhibiting lysine acetylation (HDAC inhibitors) have been reported in the scientific literature, some of which with Food andDrug Administrationapproval, for the treatment of a broad spectrum of cancer-related pathologies. SAHA  was approved to treat cutaneous T cell lymphoma . The ant+/CD133+ colon cancer cells are chemo-resistant, a characteristic that is enhanced by IL-6 stimulation and the subsequent FRA1 deacetylation. Here we showed that the combination of 5-FU with the HDAC6 inhibitor Tubastatin A, designed to target both the more committed CRC cells (by 5-FU) and the small but chemoresistant CSC fraction (by Tubastatin A), dramatically inhibited tumor growth in vivo with no significant in vivo toxicity. Hence, the combined treatment of a chemotherapeutic agent with an HDAC6 inhibitor may offer an improved and novel curative approach for CRC patients, especially in those cases where the IL-6/STAT3/FRA1 signaling axis is activated.5-FU represents one of the most common chemotherapeutic drugs in the treatment of CRC. However, its curative effects as a single agent are far from optimal. As observed in this study and supported by several previous reports, the stem-like CD44FOSL1/FRA1. HDAC6 is responsible for the deacetylation of the Lys-116 residue in FRA1. NANOG is a direct FRA1 target gene likely to play a central role in conferring increased stemness and malignancy to the cancer cell. Last, combinatorial treatment with a conventional cytotoxic agent (5-FU) and an HDAC6 inhibitor (Tubastatin A) may offer novel potential therapeutic options for CRC patients.In conclusion, upon inflammation and IL-6 secretion from the microenvironment, the STAT3 pathway becomes activated and promotes CRC stemness and malignancy through the transcriptional (promoter binding) and posttranslational (K116 deacetylation) upregulation of A total of 123 human CRC samples were collected at the Second Affiliated Hospital of Zhejiang University School of Medicine after informed consent had been given by all patients. The clinicopathological characteristics of the clinical specimens are summarized in Supplementary Table t test was used to evaluate statistical differences in different groups. Pearson correlation and Spearman rank correlation tests were computed to assess the relativity of two groups. All analyses were performed using the GraphPad Prism6 software and R x64 3.4.4 statistical software. #,*p < 0.05; ##,**p < 0.01; ###,***p < 0.001.Data represent the mean ± SD of at least three independent experiments. Animal studies were performed blinded. No animals were excluded from study results. The animal experimental groups were blinded to the experimentalists during the experiment and data collection. BALB/C nude mice  were selected for each group in a randomized fashion. The Log-rank (Mantel–Cox) test was used for survival analysis. The two-tailed All animal studies were reviewed and approved by the Laboratory Animal Welfare Committee of Zhejiang University . The procedures related to human subjects were approved by the Medical Ethics Committee of Zhejiang University School of Medicine .See Revised supplementary marked up versionRevised supplementary clean versionsupplementary figures


Comorbid attention-deficit/hyperactivity disorder (ADHD) is common in bipolar disorder and associated with worse outcomes. Cognitive testing might be a tool to identify this group. Here we compare the neuropsychological profiles of bipolar disorder patients with (BD + cADHD) and without (BD − cADHD) childhood attention-deficit hyperactivity disorder.Adult patients with BD  −  cADHD (n = 66), BD + cADHD (n = 32), and healthy controls (n = 112) were tested using a comprehensive battery of neuropsychological tests. Patients underwent rigorous diagnostic assessments for bipolar disorder and ADHD, as well as a parental interview to establish childhood ADHD.The neuropsychological profiles of the groups were similar, except that the BD + cADHD group performed significantly worse on working memory. Working memory did not differ between those in the BD + cADHD group who only had a history of childhood ADHD and those that still met criteria for ADHD in adulthood.Cognitive testing had limited power to differentiate between bipolar disorder adults with and without childhood ADHD. The BD + cADHD subgroup cannot explain the significant cognitive heterogeneity seen in bipolar disorder patients.The online version of this article (10.1186/s40345-019-0149-9) contains supplementary material, which is available to authorized users. The prevalence of comorbid attention-deficit/hyperactivity disorder (ADHD) in adults with bipolar disorder (BD) ranges between 9.5 and 28%  and without childhood ADHD (BD − cADHD) in a cohort with meticulous assessment of childhood as well as adult ADHD.The participants in this study were recruited from a bipolar outpatient clinic at the Northern Stockholm psychiatric clinic in Sweden between October 2005 and April 2008, as a part of the St. Göran Bipolar project. The project and the clinical assessment process has been described in detail elsewhere , a diagnostic instrument originally developed and used in the systematic treatment enhancement program of bipolar disorder (STEP-BD)   by the treating physician. The ADHD clinical assessments required approximately 1.5 h and were conducted by a second independent board-certified psychiatrist (E.R.). To assess current ADHD symptoms, the Adult ADHD Self-Report Scale (ASRS)  (n = 32) fulfillingi.criteria for childhood ADHD alone  (n = 14), orii.and current ADHD  (n = 18)criteria for childhood criteria for bipolar disorder type I or II, andThe bipolar disorder without childhood ADHD group (BD − cADHD) (n = 66) fulfilling criteria for bipolar disorder type I or II without a history of childhood ADHD.The present study compared the neuropsychological profiles of two groups:In supplementary analyses, we also included a healthy control group randomly selected by Statistics Sweden. Details of the recruitment, in- and exclusion criteria can be found elsewhere  as judged by the treating physician. Present mood status was assessed by administrating the Montgomery–Åsberg Depression Rating Scale (MADRS) . We also conducted complementary analyses comprising a healthy control group  and individuals fulfilling both childhood and current ADHD criteria (n = 17). We compared the WAIS Working Memory Index but found no difference . The N differs from the main analysis because some individuals did not have complete data on the WAIS working memory variable.In supplementary analyses, we compared the patient groups with a healthy control group to show the severity of cognitive impairment .There are few previous studies in this area. Our finding of small overall cognitive differences between BD-cADHD and BD + cADHD is largely in agreement with the results of Torres and colleagues (Torres et al. It is noteworthy that the few studies comparing BD and ADHD with respect to cognition suggest that differences were directly or indirectly attributable to working memory impairment in ADHD (Torralva et al. Drilling deeper into the BD + cADHD group, we found no difference in working memory between those who met criteria for ADHD in childhood as well as adulthood, and those who only did so in childhood. This indicates that adults with BD with a history of childhood ADHD are likely to show working memory impairments regardless of current ADHD status.Our findings have three important clinical implications. First, previous studies have found that BD with comorbid ADHD is associated with worse course of illness, calling for instruments to readily identify this group clinically (Rydén et al. Second, our results show that childhood ADHD was associated with impaired working memory in BD regardless of whether the diagnosis persisted into adulthood or not. This aligns with studies indicating that the mere history of childhood ADHD is sufficient to generate a worse course of bipolar illness (Rydén et al. Third, cognitive impairments observed in BD patients as a group have been attributed to one or more subgroups with more pronounced impairment (Burdick et al. Some limitations of this study should be considered. First, although the sample size was on par with previous studies in the area, a limited number of study subjects might have affected the possibility to detect group differences with ensuing risk of type II errors. Second, we did not correct for multiple comparisons; doing so would revoke the group difference in working memory. Moreover, in the supplementary analyses where a healthy control group was added, working memory was no longer statistically significantly between the bipolar disorder groups. Third, ADHD is a childhood onset diagnosis that in this study was made in retrospect. Even though we used validated instruments to assess childhood symptoms, conducted a separate diagnostic interview focusing on ADHD, and even included an interview with next-of-kin when possible, this study design is susceptible to recall bias. Fourth, although not statistically significant, there were more BD II patients in the BD + cADHD group. We did not find any differences between BD I and BD II in a previous study (Sparding et al. Cognitive performance was similar in bipolar disorder patients with and without ADHD in childhood, except that the former group performed worse on working memory. This means that comorbid ADHD cannot explain the cognitive heterogeneity seen in bipolar disorder patients, and that cognitive testing is not likely to be useful for differentiating between BD with and without ADHD.Additional file 1. Additional tables.


Background: Previous studies have reported a high prevalence of attention deficit hyperactivity disorder (ADHD) among people living in detention (PLD) corresponding to a five- to ten-fold increase compared to the general population. Our main study objective was to provide an updated ADHD prevalence rate for PLD, including PLD in psychiatric units. Sub-objectives included (i) comparing different ways of assessing ADHD, including DSM-5 criteria and (ii) identifying which types of PLD are more likely to have ADHD.Methods: We conducted a systematic review and meta-analysis following the PRISMA guidelines and the MOOSE checklist. PubMed/Medline, PsycINFO, and Web of Sciences were searched combining “ADHD” and “prison” keywords and synonyms for articles published between January 1, 1966 and January 2, 2018. Potential sources of variation to the meta-analytic ADHD prevalence rate were investigated using meta-regressions and subgroups analyses.Results: The meta-analysis pooled 102 original studies including 69,997 participants. The adult ADHD prevalence rate was 26.2% . Retrospective assessments of ADHD in childhood were associated with an increased prevalence estimate . There was no significant difference in the prevalence estimate between screenings and clinical interviews in adulthood. Only three studies used the DSM-5 definition of ADHD and results were non-significantly different with other DSM versions. We found no difference according to participants' characteristics.Conclusion: Our results confirmed the high prevalence rate of ADHD among PLD, corresponding to a five-fold increase compared to the general population. In light of such high ADHD prevalence, our results reinforce the importance of addressing this critical public health issue by (i) systematically offering ADHD screening and diagnosis to all individuals entering detention, and (ii) delivering treatment, monitoring, and care for ADHD during and after detention. These strategies may help reduce recidivism and reincarceration, as well as violence in detention settings, in addition to improving the health and wellbeing of people living in detention. Additionally, our study suggests that using screening scales may be a reliable way of assessing ADHD, although caution is needed because a complete evaluation by an experienced clinician is required to provide a formal diagnosis. Attention deficit hyperactivity disorder (ADHD) is a disorder characterized by difficulties paying attention, poor impulse control, and hyperactive behaviors. ADHD starts in early childhood and persists in adulthood in 40–60% of cases . There iADHD is associated with an increased risk of having judicial contact at a younger age, including rule-breaking behaviors, delinquency, criminality, and recidivism –7. ADHD To date, the only meta-analysis reporting ADHD prevalence in incarcerated populations included studies published until 2012. The study identified a five- to ten-fold increase in prevalence of ADHD compared to the general population : 25.5% cADHD was introduced for the first time in the second version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-II), as “kyperkinetic disorder of childhood” . It emphTo date, no systematic review and meta-analysis has provided an overview of how DSM-5 criteria may have affected the prevalence rate of ADHD, especially among PLD. Furthermore, a recent article questioned the reliability of ADHD prevalence rate among PLD, as some major methodological shortcomings, such as self-reported assessments or non-representative sampling, may have resulted in high prevalence rates . EvidencThis meta-analysis aimed to provide an updated estimate the prevalence rate of ADHD in PLD over the past three decades, including articles published since 2012. Sub-objectives included (i) comparing different ways of assessing ADHD and in particular investigating whether the DSM-5 resulted in an increased prevalence rate of ADHD, and (ii) identifying which characteristics of PLD were more likely to be associated with ADHD .The systematic review and meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  and the All studies investigating ADHD in PLD were eligible for this systematic review. In addition, articles were eligible if they (i) reported an empirical study, (ii) were written in English, and (iii) were published in a peer-reviewed journal.*” or “custod*” or “detention” or “crim*” or “offend*” or “correctional” or “forensic” or “penal institution.” Published meta-analyses on the subject identified in the search were hand-searched for other relevant studies using their reference lists and studies quoting them. These meta-analyses were excluded from the calculation of prevalence estimates. Reference lists of retrieved studies were also hand-searched.We searched Pubmed/Medline, PsycINFO, and Web of Sciences from their inception date until January 2, 2018. We used the terms “attention deficit hyperactivity disorder” or “ADHD” and “prison” or “prisoner” or “inmate” or “detaineAfter article duplicates were removed, a first round of selection was performed to exclude studies meeting exclusion criteria defined in the subsection Eligibility Criteria by screening titles and abstracts, and, if necessary, the whole article. A second round of selection was performed by reviewing the full text of articles. We excluded articles reporting on the same dataset, articles focusing only on participants with ADHD , articles reporting no prevalence rate (after unsuccessful correspondence with the corresponding author), articles with mixed prevalence rates for males and females, because gender is known as an important predictor of ADHD  (for whiTwo rounds of reviewers (SB and AF/DG/MG) independently screened all the abstracts in the first selection round. In case of disagreement, consensus was achieved by discussion, and, if required, by a third-party arbitration (HW).Characteristics of studies included in the meta-analysis were extracted independently by two rounds of reviewers (SB and EV/MG/NTT) using an electronic data abstraction form on Excel. The form included the following study characteristics: (1) year of publication (we used this information instead of year of data collection because the latter was missing in 43.1% of the studies); (2) geographic location; (3) sample size; (4) study population (adults vs. youths); (5) type of detention setting ; (6) gender; (7) mean age; (8) presence of psychiatric disorders in the sample (sample of psychiatric participants vs. “ordinary” participants); (9) type of offenders ; (10) diagnostic tools , (11) criteria used for diagnosis ; and (12) ADHD prevalence rate. Mean age was included for descriptive purposes. Studies involving both gender and/or using different diagnostic criteria  were recorded as separate observations.We contacted the authors of 67 articles regarding missing information. Five authors answered but were unable to provide gender-disaggregated prevalence rates, 35 provided missing information, and 27 did not answer. Studies with missing information on other variables than prevalence rates were kept for descriptive purposes and to estimate the meta-analytic prevalence rate of ADHD—this was not an exclusion criterion. Listwise deletion was used for other analyses.Articles included in the systematic review and meta-analysis were assessed for the risk of bias using an adaptation of the Quality in Prognosis Studies including the following relevant items : 1) sam sam1 in 2. VAF is an indicator of effect size and corresponds to the percentage of the heterogeneity in the prevalence that is accounted for in each model. Analyses were performed using R 3.4.3 and the package “metafor” version 2.0.0.We first undertook a descriptive analysis of the studies. We then estimated the meta-analytic prevalence of ADHD. We provided separate prevalence estimates for studies with an adolescent/adult measure  and childhood measure. We also computed the prevalence estimate for studies using clinical interviews, the most reliable and valid way to assess ADHD. Indeed, screening tests are not diagnostic tests . They are designed to detect people at risk for the corresponding disease. Diagnostic tests establish the presence of the disease and are used to determine the need for treatment , 28. FinWe identified 916 records on PubMed/Medline, psycINFO, and Web of Sciences. After removal of 223 duplicates, 693 publications remained. We excluded 527 publications after a first screening because they did not focus on ADHD in PLD or did not report empirical findings. After further review of the remaining 166 publications, 81 were excluded: 23 articles did not report ADHD prevalence rates or gender-disaggregated prevalence rates, 8 used samples composed of participants with 100% ADHD, 47 relied on data already used in other articles, and 3 because we had no access to the full article. The manual search of published meta-analyses led to the identification of 17 other studies. A total of 102 publications were included in the meta-analysis , North America , Asia , Australia , and South America . The information on the number of studies included for each region and other characteristics are reported in the first column of in Table n = 83), while 21.1% (n = 30) used self-reported screenings of childhood ADHD and 20.4% (n = 29) self-reported screenings of adolescent/adult ADHD. A total of 16.2%  of studies with a “weak” quality: in total, 13.4% had a response rate ≤60% or a convenient sample, 24.7% excluded non-native speakers, and 16.2% excluded PLD with psychiatric or somatic disorders  , including 69,997 participants . A total of 64.7% of the studies were published in the 2008–2017 period, whereas 29.4% were published between 1998 and 2007, and 4.9% between 1988 and 1997, plus one publication in 1985 (1.0%). The 25, 50, and 75th percentiles of year distribution corresponded respectively to years 2004, 2010, and 2014. Data came from 28 countries distributed as follows: Europe : 22.7–29.6]. The childhood ADHD meta-analytic prevalence estimate assessed retrospectively in adolescence/adulthood was 41.1% (95% CI: 34.9–47.2). Data based on clinical interviews (83 study samples) showed an overall prevalence estimate of 26.7% (95% CI: 22.7–30.7). Prevalence estimates for all study samples according to the year of publication are reported in Figure n = 142 samples) are reported in Table p < 0.001) or screening of adolescent/adult ADHD .Results of meta-regressions for all studies (n = 83), the results were almost similar, with no covariate reaching the significance level . We identified 102 studies meeting study criteria (142 study samples) published from 1985 to 2017 with data collected in 28 countries. The pooling of all studies yielded an adolescent/adult ADHD prevalence rate of 26.2%, while the pooling of only those using clinical interviews found a similar rate of 26.7%. This high ADHD prevalence rate corresponds with a five-fold increase in comparison with that of the general population –14. ThesBy contrast, the retrospective assessment of ADHD in childhood was higher at 41.1%. This suggests a remission rate of 63.8%, although a study with a longitudinal design would be required to definitively confirm this. Nevertheless, the estimation corresponds to data obtained from the general population reporting remission in 40–60% of cases . Previous studies already demonstrated that methods have an effect on the prevalence estimate , 12. ThiPrevalence estimates pooled from studies using screenings for adolescence/adult ADHD were not significantly different from estimates of studies using clinical interviews. Recent studies showed that self-reported assessments of ADHD in adulthood are reliable [for example, the ADHD self-reported screening scale, ASRS, : sensitiTo our knowledge, no meta-analysis has investigated differences in estimates according to DSM versions, including the DSM-5. Unfortunately, there were only three studies using the DSM-5 version to assess ADHD among PLD. There was no significant difference between the versions of DSM, but there was probably a bias due to a lack of statistical power. Further studies should test whether there is an increase in the prevalence rate of ADHD when the DSM-5 definition is used , 21. We From a methodological point of view, the quality of the studies did not significantly affect the prevalence estimates of ADHD . Previous studies criticized methodological weaknesses in many prison , 43. HowOne of our sub-objectives was to identify which characteristics of PLD were associated with ADHD. Among the five characteristics included in our meta-analysis, none was associated with a significant increase in the ADHD prevalence rate.Although ADHD is highly comorbid with other psychiatric disorders , 44, we There were no significant differences for gender and age (adults vs. youths). This supported the previous meta-analysis of Young et al.  conducteRegarding population age, studies have reported that full remission of childhood ADHD commonly occurred in adulthood after brain maturation , 40. HowOnly methodological and PLD characteristics that were available across studies, or in most studies, were included. This may have led us to miss some important factors associated with the heterogeneity of study findings. For example, we were unable to extract precisely the type of detention . Most studies did not report ADHD treatment, which may have been helpful in understanding ADHD remission. Second, the validity and reliability of ADHD assessments may have contributed to the heterogeneity of the prevalence estimates. However, our meta-analysis took into account the overall diagnostic approach, even if the specific characteristics of the assessment scales were not included in the model. The heterogeneity of the samples used was also a possible source of variability in the prevalence estimates. This was taken into account by using random study effects. Finally, there were insufficient studies applying the DSM-5 definition of ADHD for meaningful analysis and some regions of the world were under-represented (South America), whereas other were completely missing .ADHD has been an important research focus in the last 2 decades, with 102 studies published in 28 countries regarding prevalence in prison settings around the world. In light of the high ADHD prevalence among PLD (including PLD with comorbid disorders incarcerated in psychiatric units), a five-fold increase in comparison with the general population, our results reinforce the importance of addressing this critical public health issue by (i) systematically offering ADHD screening and diagnosis to all individuals entering detention  following the most up-to-date criteria, and (ii) delivering treatment, monitoring, and care for ADHD and other psychiatric comorbidities to patients while they are in prison and after their release. These strategies may benefit PLD, prison staff, and society in general. Further studies should research the needs of this population and investigate the efficacy and effectiveness of treatment  for PDL with ADHD are required.SB, AF, PH, DD, and HW conceived the study. SB, AF, MG, EF, DG, NTT, and HW collected data. SB drafted the manuscript and performed statistical analyses. All authors excepted SB made substantial contributions for the interpretation of data and revised the manuscript critically for important intellectual content. All authors approved the final version to be published and agreed to be accountable for all aspects of the work related to its accuracy and integrity.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DD and handling editor declared their shared affiliation at time of review.