ACO1 and IREB2 are two homologous cytosolic regulatory proteins, which sense iron levels and change iron metabolism–linked molecules. These two genes were noticeably decreased in kidney renal clear cell carcinoma (KIRC), which confer poor survival. Meanwhile, there is a paucity of information about the mechanisms and clinical significance of ACO1 and IREB2 downregulation in renal cancers.via several bioinformatics platforms. Clinical and pathological information was utilized to stratify cohorts for comparison. Patient survival outcomes were evaluated using the Kaplan–Meier plotter, a meta-analysis tool. The correlations of ACO1 and IREB2 with ferroptosis were further evaluated in The Cancer Genome Atlas (TCGA)–KIRC database. Tumor immune infiltration was analyzed using the CIBERSORT, TIMER, and GEPIA data resources. ACO1 antagonist sodium oxalomalate (OMA) and IREB2 inhibitor sodium nitroprusside (SNP) was used to treat renal cancer ACHN cells together with sorafenib.The expression profiles of ACO1 and IREB2 were assessed using multiple public data sets KIRC patients with low ACO1 or IREB2 contents exhibited a remarkably worse survival rate in contrast with those with high expression in Kaplan–Meier survival analyses. Meanwhile, ACO1 and IREB2 regulate autophagy-linked ferroptosis along with immune cell invasion in the tumor microenvironment in KIRC patients. Blocking the activation of these two genes by their inhibitors OMA and SNP ameliorated sorafenib-triggered cell death, supporting that ACO1 and IREB2 could be participated in its cytotoxic influence on renal cancer cells.ACO1 and IREB2 downregulation in renal cancers were correlated with cancer aggressiveness, cellular iron homeostasis, cytotoxic immune cell infiltration, and patient survival outcomes. Our research is integral to verify the possible significance of ACO1 and IREB2 contents as a powerful signature for targeted treatment or novel immunotherapy in clinical settings. Renal cell carcinoma (RCC) accounts for approximately 3% of all human systemic malignancies and 85%–90% of all primary malignancies in the adult kidney . New casIron proved to be one of the most plentiful trace elements, which works an essential role for cell functions . The defin vivo when suffering hepatic inflammation . ACHN cells were cultured in Eagle’s Minimum Essential Medium enriched with 10% Fetal Bovine Serum (FBS) along with 1% penicillin/streptomycin under 37°C along with 5% COG-CLONE supplied the small chemicals sorafenib (GS0220) along with sodium nitroprusside (GN0200).Sigma-Aldrich provided us with rapamycin (V900930). ABCAM supplied antibodies to ACSL4 (ab227256), beta-tubulin (ab6046), Beclin-1 (ab210498), LC3 (ab192890), FTH (ab183781), ATG12 (ab155589), and NCOA4 (ab86707). Secondary antibodies linked to HRP, as well as chemiluminescent reagents, were bought from Santa Cruz Biotech .Initially, all chemicals were dispersed in dimethylsulfoxide (DMSO) as a stock solution. The figure legends indicated the chemical treatment duration along with the final levelsvia Western blotting  solutions and 100 µl of medium each well. The OD values were determined at 450 nm after 1.5-h incubation. To verify the association of cell death with autophagy and ferroptosis, the classical autophagy markers  and ferroptosis markers  were explored blotting . We harvCells were seeded in a six-well cell culture plate as indicated. The cell lysis was then collected and centrifuged. For the malondialdehyde (MDA) assay, the procedure was performed according to the instruction . For the GSH assay, the supernatant was collected and detected according to the specification of the GSH assay kit .http://timer.cistrome.org/) employed to analyze immune infiltrates in numerous kinds of cancers. We utilized the “Exploration-Gene DE” module to assess ACO1 along with IREB2 expressions in diverse malignancies. TIMER2.0 was adopted to explore the relationship of ACO1 and IREB2 with immune cell invasion in the KIRC. Using The Cancer Genome Atlas (TCGA) data resource, the “Immune-Gene” module can explore the relationship of ACO1 and IREB2 contents with immune cell invasion levels . We adopted the “Exploration-Gene Corr” module in TIMER2.0 to explore the link between ACO1 and IREB2 contents with numerous gene biomarker sets of immune cells. Purity-adjusted partial Spearman’s correlation along with the significance level was utilized to assess the relationships of ACO1 and IREB2 contents with immune invasion .TIMER2.0 constitutes a comprehensive web tool  is a web-based resource offering in-depth assessments of transcriptome data from TCGA, MET500, and Clinical Proteomic Tumor Analysis Consortium data. UALCAN was used to explore the mRNA along with the protein contents of ACO1 and IREB2 genes in KIRC.UALCAN (http://www.proteinatlas.org/) harbors extensive proteome along with transcriptome data for diverse human samples consisting of cell, tissue, and pathology atlases. The protein immunohistochemistry of IRP1 and IRP2 in normal kidney and renal cancer was obtained from this online website.The Human Protein Atlas (HPA) data resource (www.xiantao.love) is an online platform that contains TCGA data resource. The XIANTAO platform was used to analyze the expression of ACO1 and IREB2 and the relationship of ACO1, IREB2 with numerous clinicopathological indices  of TCGA-KIRC. The patient samples were stratified into two groups on the basis of the median expression (high-ACO1 and IREB2 expression and low-ACO1 and IREB2 expression) to assess the OS  and DSS  along with PFI  with HR) with 95% Cis, as well as log-rank P-values. We chose the XIANTAO platform for assessing the forecast-worth of ACO1 and IREB2 in KIRC based on diversity clinicopathological parameters . Then, we created a concise nomogram and predicted the OS of KIRC in this platform, including nine factors. In addition, calibration curves were used for evaluating the predictive accuracy of the nomogram.The XIANTAO platform (rameters 2. Thehttp://genemania.org/) was applied to construct the ACO1 and IREB2 crosstalk network. We adopted the STRING data resource (https://string-db.org/cgi/input) to create a protein–protein interaction (PPI) network of ACO1 and IREB2. Additionally, the relationship of ACO1 and IREB2 with iron metabolism-linked genes in KIRC were explored via Spearman’s correlation coefficient on the XIANTAO platform (www.xiantao.love).The GeneMANIA data resource (http://gepia2.cancer-pku.cn/#index) a user-friendly web resource for analyzing gene expression of the TCGA along with GTEx data resources. In the module “Similar Genes Detection,” the coexpression genes of ACO1 and IREB2 were evaluated using TCGA-KIRC data sets. The top 300 coexpression genes with the Pearson correlation coefficient above 0.70 were utilized for GO (Gene Ontology) term and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis on the XIANTAO platform (www.xiantao.love). GO analysis constitutes a bioinformatics tool to explore the BPs , CCs (cellular components), and MFs (molecular functions) linked to ACO1 and IREB2. Then, in order to identify the potential pathways referring to ACO1 and IREB2 in KIRC, the KEGG pathway enrichment analysis was applied. Furthermore, the relationships of ACO1 and IREB2 with PD-1, LAG3, PD-L1, HAVCR2, and CTLA-4 were explored via Spearman’s correlation coefficient in the “Correlation Analysis” module.GEPIA2 (http://www.autophagy.lu/index.html) is a public data resource harboring data on the so- far-described human genes as involved in autophagy. A total of 222 genes were abstracted from the HADb. The differentially expressed autophagy-linked genes in KIRC were picked up and used for further analyses combined with ACO1 and IREB2.HADb (http://www.zhounan.org/ferrdb/index.html) constitutes a data resource of ferroptosis modulators along with biomarkers, as well as ferroptosis–disease relationships. We abstracted 259 genes from the FerrDb. The differentially expressed ferroptosis-linked genes in KIRC were picked up for further analyses combined with ACO1 and IREB2.FerrDb  constitutes an online data resource harboring gene expression data and survival information coupled with the immune cell infiltration of 530 clinical KIRC patients.The KM Plotter data resource  constitutes an integrated data resource for tumor–immune system crosstalks. The links of ACO1, IREB2 with PD-1, LAG3, PD-L1, and CTLA-4, as well as HAVCR2, were explored via Spearman’s correlation coefficient in the “Immunomodulator” module.TISIDB (www.xiantao.love), the OS of ACO1, as well as IREB2 in CNA, was explored.The cBioPortal for Cancer Genomics supports visualization and abstraction, along with the assessment of the large-scale cancer genomics data set. We adopted the cBioPortal to further explore the TCGA-KIRC data sets (Firehose Legacy) with 538 cases. The “Plots” module was adopted to determine the changes in the copy numbers of ACO1 along with IREB2 in KIRC. On the XIANTAO platform (Every cell-based experiment was done in triplicate. The mean + SEM was employed to display quantitative data acquired from bioinformatics platforms (SEM). The Western blotting photos were representative of several blots. The bioinformatics platforms were used to create the graphic images. The analysis of variance (ANOVA) method was used for multiple group data analysis, followed by Student’s t-test or Wilcoxon rank-sum test for comparison between two groups. GraphPad Prism  was used for statistical calculations, with P < 0.05 signifying statistical significance.via the TIMER2.0 data resource. Both ACO1 and IREB2 genes were lowly expressed in breast-invasive carcinoma (BRCA), rectum adenocarcinoma (READ), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), KIRC, pheochromocytoma, and paraganglioma (PCPG), as well as uterine corpus endometrial carcinoma (UCEC) assessed with the matching non-tumorous tissues . Concurrently, we also noticed that lower transcript contents of ACO1 and IREB2 was detected in KIRC tissues versus healthy kidney tissues in the UALCAN data resource . In addition, ACO1 and IREB2 contents in KIRC samples and neighboring tissues were assessed in data directly abstracted from the TGCA data resource. ACO1 and IREB2 contents were noticeably decreased in KIRC tissues . These findings illustrate that ACO1 and IREB2 expression are reduced in KIRC and indicate that ACO1 and IREB2 may harbor an essential modulatory role in KIRC advancement.The transcript contents of ACO1 and IREB2 in human solid cancers was first evaluated  tissues . Concurr tissues . Concurr. Consistently, the expression of ACO1 and IREB2 protein were evidently downregulated in KIRC tissue compared with healthy tissue in the UALCAN database .The protein expression level of ACO1 and IREB2 were further investigated in renal cancer using the HPA database, and we noticed that the ACO1 and IREB2 protein level turned evidently down in the tissues of KIRC contrasted with healthy kidney . Consist. In line with sex, ACO1 expression was noticeably downregulated in TCGA-KIRC samples among both men and women when contrasted to the matching normal controls , but not IREB2 . Based on the TNM stage, ACO1 and IREB2 expression were significantly decreased in KIRC patients with T3 and 4 relative to those with T1 and 2 . ACO1 expression, but not IREB2 expression, was lower in KIRC patients that are classified as N0 or N1 compared with normal controls . A noticeable decrease in ACO1 expression was watched among KIRC patients classified as M0 or M1 contrasted with normal controls, while a remarkable drop in the IREB2 level was witnessed in KIRC patients with M1 relative to those with M0 . In terms of age, ACO1 expression, but not IREB2 expression, was remarkably reduced in the KIRC tissues of patients from different age groups compared with normal controls . In addition, ACO1 expression was dramatically decreased in KIRC patients in grade 1, 2, 3, and 4 compared with normal controls, while IREB2 expression was significantly reduced in KIRC patients in grade 3 and 4 compared with KIRC patients in grade 1 and 2  including ACO1 and IREB2 . Furthermore, a favorable consistency among the prediction and actual study was illustrated by the calibration plot of the nomogram in the first-year, third-year, and fifth-year OS in the TCGA-KIRC cohort .We develop a nomogram that illustrate the ACO1- and IREB2-related danger signature and several independent clinical indexes , which may provide doctors a quantitative method to assess the predicting outcomes of KIRC patients . We also. We also, while low IREB2 expression was noticeably accompanied with unsatisfied OS, DSS, and PFI in KIRC patients . In different tumor stages, low ACO1 expression was accompanied with worse OS, DSS, and PFI only in stage 1 and 3 patients with KIRC , while low IREB2 expression was strongly accompanied with worse OS in stage 1,worse DSS in stage 3, and worse PFI in stage 1 and 3 patients with KIRC . Based on the histologic grade, low ACO1 and IREB2 expression were remarkably accompanied with worse OS, DSS, and PFI in grade 2 and 3 patients with KIRC .To further study the predictive value and possible mechanism of ACO1 and IREB2 differential expression in KIRC, we used the Kaplan–Meier database to seek the potential links between ACO1 and IREB2 mRNA expression and clinical features. Low ACO1 expression was remarkably accompanied with worse OS, DSS, and PFI only in the female cohort in KIRC , while l, while l, while l, while l, while l, while a significant clue between low IREB2 expression and worse OS, DSS, and PFI were detected in AJCC stage T-1 and T-3 KIRC patients . Low ACO1 expression was noticeably accompanied with unsatisfied OS and DSS in N0 KIRC patients , while low IREB2 expression was noticeably accompanied with worse OS, DSS, and PFI in N0 patients with KIRC . Low ACO1 and IREB2 expression was remarkably accompanied with worse OS, DSS, and PFI in M0 patients with KIRC .In terms of the TNM stage, a noticeable link between low ACO1 expression and worse OS, DSS, and PFI was studied in American Joint Committee on Cancer (AJCC) stage T-1 and T-3 KIRC patients , while a, while a, while a, while a, while a, while a significant link between poor IREB2 expression and worse OS, DSS, and PFI was observed in both ectopic and normal serum calcium patients with KIRC . Furthermore, we noticed a strong link between poor ACO1 and IREB2 expression and worse OS, DSS, and PFI in both ectopic and normal hemoglobin patients with KIRC . Furthermore, low ACO1 and IREB2 expression was strongly accompanied with unsatisfied OS, DSS, and PFS in the cohort with left or right laterality KIRC . These phenomena imply that ACO1 and IREB2 mRNA expression could show a remarkable forecast-value in patients with KIRC.In addition, a significant clue between low ACO1 expression and worse OS, DSS, and PFI were observed in ectopic serum calcium patients with KIRC , while a, while a, while a, while a, while avia GeneMania. It reminded that the 20 most changeable genes were remarkably accompanied with ACO1 and IREB2, consisting of SLC9A3R1, FBXL5, and ACO2 . Further functional analysis revealed that these genes were remarkably accompanied with the iron–sulfur cluster binding and iron ion homeostasis . We created a PPI network of ACO1 and IREB2 with the STRING data resource , which showed 176 edges and 22 nodes, consisting of GLRX3 and CUL1 along with SKP1 . It was suggested in functional enrichments that these genes were noticeably accompanied with the cellular iron ion homeostasis, iron-sulfur cluster binding, and iron uptake and transport . The overlap of top 20 genes and top 20 proteins predicted by GeneMania and STRING were ACO2, FBXL5, and IDH2 . Furthermore, we used the TCGA data resource to investigate the relationship of ACO1 and IREB2 with iron metabolism-linked genes; and then, we found that ACO1 was directly, as well as noticeably, linked to IREB2, TFRC, HFE, SLC40A1, and FTH1 but inversely associated with HAMP, HJV, FGF23, GMFG, TF, TMPRSS6, ACD, and TFR2 in KIRC . IREB2 was directly, as well as noticeably, linked to ACO1, TFRC, HFE, and SLC40A1; however, IREB2 was inversely associated with HAMP, HJV, GMFG, TF, TMPRSS6, FTL, ACD, and TFR2 in KIRC . These results remind that ACO1 and IREB2 are associated with the modulation of iron uptake, transport, and cellular iron ion homeostasis.The gene–gene crosstalk network for ACO1, IREB2, and the modified adjacent genes was constructed and ACO2 . Furtherand ACO2 . Furtherand ACO2 . Furtherand ACO2 . Furtherand ACO2 . Furtherand ACO2 . Furtherand ACO2 . Further. Then, we used KEGG and GO enrichment analyses for 300 genes coexpressed with ACO1 and IREB2 to explore the ACO1- and IREB2-linked cascades along with biological functions. The top 20 essential terms of BP, MF, and CC enrichment analyses are listed . The KEGG cascades for ACO1, IREB2 and its linked genes are illustrated in . These results strongly imply that ACO1 and IREB2 participated in the modulation of autophagy in KIRC patients.Our team determined genes directly or inversely coexpressed with ACO1 and IREB2 by mining data from the GEPIA2 data resource. The 20 most plentiful genes that coexpressed among ACO1 and IREB2 in KIRC are shown . Then, w. Then, w. Then, w. The 45 differentially stated autophagy-related genes, including 36 upmodulated and 9 downmodulated genes, were presented in volcano plots and heat maps .To further evaluate the correlation of ACO1 and IREB2 expression with autophagy, a total of 222 autophagy-related genes were obtained from the Human Autophagy Database (HADb). Compared with the normal controls, we identified the 45 differentially stated autophagy-related genes in TCGA-KIRC by the criteria of P-value < 0.05 and absolute logarithmic (base 2) fold-change value >1.0 . The 45 . To further explore the expression correlation of ACO1, IREB2 with the 45 genes in KIRC, correlation analysis was performed built on TCGA database. ACO1 was noticeably correlated with 38 of 45 autophagy genes in KIRC, and IREB2 was significantly correlated with 37 of 45 autophagy genes in KIRC .To figure the interactions among ACO1, IREB2 and the 45 differentially stated autophagy-related genes, we performed PPI analysis. The results illustrated that ACO1 and IREB2 were cointeracted with HIF1A and GAPDH . To furt. In KEGG enrichment analysis, the ACO1, IREB2 and 45 differentially expressed autophagy-linked genes mainly participated in the procedure of autophagy . In addition, ACO1 and IREB2 were predicted to correlate with the p53 signaling cascade and PD-L1 level and PD-1 checkpoint cascade in solid cancer , which supports the relationship of ACO1, IREB2 with immune invasion in KIRC.We choose GO along with KEGG enrichment analysis in the XIANTAO platform to scrutinize the possible biological roles of these variously expressed autophagy-associated genes combined with ACO1 and IREB2. The data demonstrated that the essential GO terms participated in the process utilizing autophagic mechanism, autophagy, and macroautophagy ; autophagosome and autophagosome membrane (cellular component); and ubiquitin-like protein ligase binding and ubiquitin protein ligase docking (molecular function) . In KEGG. In KEGG and autophagy , the correlations of ACO1 and IREB2 with ferroptosis were further evaluated in the TCGA-KIRC database. A total of 259 ferroptosis-related genes were learned from the Ferroptosis Database (FerrDb). Compared with the normal controls, the 150 ferroptosis-associated genes expressed distinctively, including ACO1, in TCGA-KIRC were distinguished using the criteria of P-value <0.05 and absolute logarithmic (base 2) fold-change value >0.325 . The 150 differentially expressed ferroptosis-related genes, along with 87 upmodulated genes and 63 downmodulated genes, are illustrated in volcano plots .Given the potential association with cellular iron ion homeostasis  and auto and auto and auto and auto. A total of 111 genes were correlated with both ACO1 and IREB2. To further determine the interactions among ACO1, IREB2 and the 111 ferroptosis-related genes, PPI analysis was performed. The results demonstrated that ACO1 and IREB2 were cointeracted with NFS1, FTH1, HAMP, FH, CS, ISCU, EPAS1, NCOA4, HIF1A, GOT1, and HMOX1 .To further explore the expression correlation of ACO1, IREB2 with the 150 genes in KIRC, correlation analysis was performed based on the TCGA database. ACO1 was noticeably accompanied with 121 of 150 ferroptosis-related genes in KIRC, and IREB2 was significantly correlated with 130 of 150 ferroptosis-related genes in KIRC . A total. In KEGG enrichment analysis, the ACO1, IREB2 and the 111 genes were chiefly involved in the process of autophagy-animal, mitophagy-animal, the HIF-1 signaling pathway, and ferroptosis . These results suggested that ACO1 and IREB2 were tightly associated with autophagy and ferroptosis in KIRC.The XIANTAO platform was used and GO and KEGG enrichment analyses were conducted to evaluate the possible biological functions of the 111 genes accompanied with ACO1 and IREB2. The outcomes suggested that the essential GO-enriched terms participated in procedure response to oxidative stress, autophagy, and response to metal ion ; mitochondrial outer membrane, autophagosome, and vacuolar membrane (cellular component); and ubiquitin protein ligase binding, iron ion binding, and antioxidant activity (molecular function) . In KEGG. Meanwhile, there were direct and remarkable links between the expression of IREB2 with the invasion of all six kinds of immune cells in KIRC .The links between ACO1 and IREB2 expression with six kinds of invading immune cells were explored, consisting of B cells, neutrophils, CD8+ T cells, macrophages, CD4+ T cells, and dendritic cells. The data announced that ACO1 contents had a remarkably direct association with the invasion of B cells, dendritic cells, and macrophages along with neutrophils, while there were no noticeable relationships with CD4+ T cells and CD8+ T cells in KIRC . Meanwhi. Moreover, in KIRC, IREB2 was directly linked to Tcm cells, eosinophils cells, T helper cells, mast cells, neutrophils, Th17 cells, effector memory T cells (Tem), and Tgd, along with macrophages; however, IREB2 was inversely linked to NK CD56 (bright) cells, cytotoxic cells, TReg, CD8 T cells, NK CD56(dim) cells, activated DC (aDC), pDC, T cells, and B cells, along with Th1 cells .We assessed the links between ACO1 and IREB2 and immune invasion by using the accomplished data resource CIBERSORT, which reflects the influences of ACO1 and IREB2 on the tumor microenvironment (TME). In KIRC, ACO1 was established to be directly linked to eosinophils, neutrophils, macrophages, immature dendritic cells (iDC), mast cells, gamma-delta T cells (Tgd), central memory T cells (Tcm), type 17 T helper cells (Th17 cells), and dendritic cells (DC), along with type 2 T helper cells (Th2 cells); however, ACO1 was inversely linked to cytotoxic cells, NK CD56(bright) cells, plasmacytoid dendritic cells (pDC), CD8 T cells, and regulatory T cells (TReg), as well as NK CD56(dim) cells . Moreove.We introduced the TIMER data resource to verify the links between ACO1 and IREB2 expression and multiple immune signatures in KIRC, which deepen our comprehension of ACO1 and IREB2 crosstalk with the immune response. We used the genes listed in the . When using the TIMER data resource, we noticed that the ACO1 and IREB2 contents were strongly accompanied with 33 of 38 T-cell biomarkers in KIRC after adjusting for tumor purity .We also studied the potential link between ACO1 and IREB2 contents and diverse functional T cells, consisting of resistant memory T cells, Th1, effector Tregs, effector memory T cells, Th1-like, Treg, naïve T cells, resting Tregs, Th2, and effector T cells along with exhausted T cells . When us. These conclusions deeply support that ACO1 and IREB2 expression are strongly linked to immune infiltration and imply that ACO1 and IREB2 play important roles in immune escape in the KIRC microenvironment.Then, the interrelationship of ACO1 and IREB2 expression with famous T-cell checkpoints, consisting of PD-1, HAVCR2, PD-L1, LAG3, and CTLA-4, were developed in the GEPIA data resource. Both ACO1 and IREB2 expression were strongly accompanied with the contents of PD-1, LAG3, PD-L1, CTLA-4, and HAVCR2 in KIRC . Consistently, KIRC patients who express a low level of IREB2 and poor infiltration state of mesenchymal stem cells, type 1 T helper cells, natural killer T cells, and type 2 T helper cells had a worse prognosis. There were noticeable links between ACO1 contents and the prognosis of KIRC in the cohort with diverse levels of basophils, eosinophils, B-cells, CD4+ memory T-cells, regulatory T-cells, and CD8+ T-cells, along with macrophage infiltration . These conclusions imply that the prognosis of KIRC patients may be affected by the immune infiltration of ACO1 and IREB2.Accordingly, ACO1 and IREB2 expression is closely accompanied with immune invasion and even worse prognosis in KIRC; we explored if the prognosis of KIRC because of immune invasion was modulated by ACO1 and IREB2 expression. We developed prognosis assessments built on the contents of ACO1 or IREB2 in KIRC in corresponding immune cell subgroups. As exhibited in . Consist. Consist. Previous reports in line with this conclusion that sorafenib-triggered cell death is an autophagy response  levels in ACHN cells treated with sorafenib . These data imply that ACO1 and IREB2 participated in sorafenib-induced ferroptosis and blocking ACO1 or IREB2 could attenuate sorafenib’s anticancer influence. Alternatively, overexpressing or enhancing the enzyme activity of ACO1 and IREB2 might be novel treatment targets for sorafenib-resistant KIRC patients.Sorafenib is a multikinase inhibitor, which is commonly used for treating advanced kidney and liver cancer. It has been shown to induce ferroptosis in renal cancer cells . A previresponse . Further. Previou. Previou. The deletion in CNA of ACO1 and IREB2 predicted a lower gene expression, while the amplification in CNA of ACO1 and IREB2 predicted a higher gene expression . Notably, KIRC patients with ACO1 or IREB2 deletion was found to be a predictor of poorer survival outcomes than those without deletion  takes place in 85% of these cases . Normall2. Pres of RCC . Afterwavia activating autophagy in NSCLC, while the knockdown of IREB2 remarkably weakened curcumin-caused tumor suppressor ability in lung cancer cells , chromophobe RCC (CRCC), and other histological types of RCC in our research. Overall, our results confirm that ACO1 and IREB2 could act as a potential powerful prognostic biomarker for KIRC. Furthermore, we studied the underlying evidence pointing that ACO1 and IREB2 regulate autophagy-linked ferroptosis along with immune cell invasion in the TME in KIRC patients. Consequently, these findings own an underlying value in deepening our current comprehension of not only the characters of ACO1 and IREB2 but also their translational use in KIRC prognosis and targeted therapy.For advanced RCC, multikinase repressors, for instance, sorafenib, are the first line of treatment . StrikinPublicly available datasets were analyzed in this study. This data can be found here:http://timer.cistrome.org/http://ualcan.path.uab.edu/analysis.htmlhttp://www.proteinatlas.org/http://www.xiantao.lovehttp://genemania.org/http://gepia2.cancer-pku.cn/#indexhttp://www.autophagy.lu/index.htmlhttp://www.zhounan.org/ferrdb/index.htmlhttps://kmplot.com/analysis/http://cis.hku.hk/TISIDB/index.php.LCD and ZT designed the study. ZJH, TH and YHY carried out the experiments of cytobiology. XZY, CTY and WHY conducted the follow-up study. CQ, YJK and ZQZ participated in acquisition of data. XKY, WHY and CMK took part in collection of clinical samples. GWB, XM and BJM analyzed the experimental data. ZJH drafted the manuscript. YC, DHF and HZP participated in the revising of the manuscript. All authors read and approved the final manuscript.This work was supported by National Natural Science Foundation of China (No. 81902991), Guangzhou Science and Technology Plan Project (No. 202102021056) and Youth Project of The Third Affiliated Hospital of Southern Medical University (No. QD2019N010).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.


TikTok is a free mobile application, that enables users to create short videos. TikTok has an estimated one billion monthly active users, comprised of a mostly younger audience. There has been a noticed rise in content discussing ADHD – hashtag ADHD on TikTok has 6.3 billion views. The discussions continue on Twitter, where users are reporting watching TikTok content explaining ADHD symptomatology, subsequently relating to the condition and requesting referrals to specialist psychiatry services. This study aims to identify key themes in discussions around TikTok and ADHD, and its ramifications.This study’s objective is to discuss the relationship between viewing ADHD content of TikTok and self-diagnoses of ADHD in young people.In our study, Twitter posts were identified with the words ‘ADHD’ and ‘TikTok’ and established key themes relating to self-diagnosis of ADHD.Numerous tweets were found discussing individual’s experiences of self-diagnosis of ADHD after watching TikTok videos and relating with the symptomology. Furthermore, many users discussed their efforts to seek diagnosis from psychiatrists. These posts highlighted positive discussion of mental health, and the improvement in quality of life since diagnosis.Many young people are self-diagnosing ADHD after viewing TikTok videos. This may improve mental health stigma, however the expertise of the video creators should be scrutinised. Furthermore, the impact on already stretched waiting lists should be considered, with individuals who’s perceived ADHD traits are not impacting on their quality of life.No significant relationships.


ADHD-C were characterized from ADHD-I by reduced network connectivity differences within the DMN. Additionally, reduced connectivity within the DMN was negatively associated with ADHD-RS hyperactivity-impulsivity subscale score. Compared with controls, ADHD-C but not ADHD-I differed by reduced connectivity within the DMN; inter-network connectivity between the DMN and somatomotor networks; the DMN and limbic networks; and between the somatomotor and cingulo-frontoparietal, with ventral attention and dorsal attention networks. However, graph-theoretical measures did not significantly differ between groups. These findings provide insight into the intrinsic networks underlying phenotypic differences between ADHD types. Furthermore, these intrinsic functional connectomic signatures support neurobiological differences underlying clinical variations in ADHD presentations, specifically reduced within and between functional connectivity of the DMN in the ADHD-C type.Neuroimaging studies have revealed neurobiological differences in ADHD, particularly studies examining connectivity disruption and anatomical network organization. However, the underlying pathophysiology of ADHD types remains elusive as it is unclear whether dysfunctional network connections characterize the underlying clinical symptoms distinguishing ADHD types. Here, we investigated intrinsic functional network connectivity to identify neural signatures that differentiate the combined (ADHD-C) and inattentive (ADHD-I) presentation types. Applying network-based statistical (NBS) and graph theoretical analysis to task-derived intrinsic connectivity data from completed fMRI scans, we evaluated default mode network (DMN) and whole-brain functional network topology in a cohort of 34 ADHD participants (aged 8–17 years) defined using DSM-IV criteria as predominantly inattentive (ADHD-I) type ( The field of brain functional connectomics is rapidly expanding, revealing insights into large-scale brain networks underlying Attention Deficit Hyperactivity Disorder (ADHD) , a prevaClinically characterized by symptom clusters of inattentiveness, hyperactivity-impulsivity, or both, ADHD presentation types are classified as predominantly inattentive (ADHD-I), predominantly hyperactive-impulsive (ADHD-HI), or combined (ADHD-C) , respectDisruptions of intrinsic brain network connectivity and task-based functional connectivity involving the default mode network (DMN), cingulo-opercular, frontoparietal and executive control [referred to as the cingulo-frontoparietal (CFP) attention network in this study], ventral attention network (VAN), and the somatomotor [also known as the somatosensory network (SMN)] are found to characterize ADHD . StructuFindings from functional imaging studies have also provided a window into the neural pathways underlying the clinical presentations of ADHD, irrespective of shared core clinical symptoms . While ra priori specific networks and seed selection, predominantly in cohorts inclusive of all ADHD types or the non-disclosure of ADHD type . The iSPOT-A inclusion/exclusion criteria protocols outlining participant recruitment, diagnostic measures and procedures for iSPOT-A has been previously published . In addin = 5) and ADHD-I group (n = 2) had a diagnosis of oppositional defiant disorder (ODD). All (n = 34) ADHD participants were medication-free at testing; 16 were medication naïve; 18 treatment-experienced withdrew from methylphenidate for at least five half-lives. All participants were fluent in English, with no history of brain injury or significant medical conditions affecting brain function  or contraindications for MRI. All participants, their guardians, or both, were provided with a written informed consent form to participate in the research, per National Health and Medical Research Council (NHMRC) guidelines and institutional review board ethical guidelines .Task functional magnetic resonance imaging (fMRI) data collected at Westmead Hospital, Sydney, NSW, Australia, as part of the baseline fMRI scans from the two studies were available for 34 participants with ADHD  and 39 age and gender-matched healthy control participants . Confirmation of ADHD diagnosis (DSM-IV criteria), subtype , was measured by the Mini International Neuropsychiatric Interview (MINI Kid) , the AttStructural and functional magnetic resonance images (MRI) were acquired by the Department of Radiology, Westmead Hospital, Sydney, NSW, Australia, on a 3T GE Signa HDx scanner  using an eight-channel phased-array head coil. The image acquisition and pre-processing details have been previously described , 2020 anThree-dimensional (3-D) T1-weighted structural magnetic resonance images were acquired in the sagittal plane using a 3D SPGR sequence . The sequence was used to normalize the functional data to MNI standard space.1 and in-house scripts. Creation of a temporal mask for each censored volume used as regressors of no interest in the first level statistical models  package in MATLAB and VBM8 toolbox . In addil models , 2014. Tl models . Additiol models  proposedl models , 2020. Ul models .z scores using Fisher’s Z-transformation. The DMN connectivity matrix was derived using an available DMN ROI mask used in the field  for the whole-brain and the default mode network (DMN). The automated anatomical labeling (AAL) 116 atlas  defined he field  and our he field . The DMNt-test at every inter-regional connection applying a test statistic (t) threshold of 3 as a primary component-forming threshold corresponding to an uncorrected level of p < 0.001. For the DMN, we used a less stringent t threshold of 2 corresponding to an uncorrected level of p < 0.05, considering that we had fewer connections for the DMN matrix for this analysis. This was done to identify any connected components, and next, the size of each component was evaluated with the family-wise error (FWE) corrected p-value of 0.05 used with 5,000 permutations for both the whole brain and default mode network analyses.Network-based statistical analyses (NBS)  were appTo help interpret the connectivity findings in intrinsic brain network definitions, we used the Yeo 7 network atlas , a funct2  via the Benjamini-Hochberg , with corrected Hochberg  approachHochberg  toolbox3p < 0.01). Of the ADHD-I type participants, one participant qualified for seven items out of 9 hyperactive-impulsive subscale items, five items (n = 1), four items (n = 2), three items (n = 1), two items (n = 1) and ≤1 item (n = 9). Head motion censoring data showed no significant differences between the ADHD subtypes or the ADHD subtypes and controls.p = 0.035 corrected for multiple comparisons). This connectomic signature was characterized by: (1) reduced inter-network intrinsic functional connectivity: (a) between regions of the somatomotor and DMN; (b) between regions of the somatomotor, cingulo-frontoparietal, ventral attention and dorsal attention networks; (c) between regions of the DMN, and the limbic networks; and (2) reduced intra-network intrinsic functional connectivity within the DMN. No significant inter-network connectivity differences were identified between ADHD-C and ADHD-I or ADHD-I and controls, corrected for multiple comparisons.The whole-brain NBS analysis identified a connectomic signature comprising 24 inter-regional connections across 21 nodes between ADHD-C and controls  within the DMN characterized by: reduced intra-network intrinsic functional connectivity for the ADHD-C group for anterior-posterior DMN regions and between the cerebellar tonsils and para-hippocampus, and between the cerebellar tonsils and superior frontal and posterior cingulate cortices. No within-network differences were found between either ADHD type compared with controls.Network-based statistical analysis identified a connectomic signature comprising 9 connections across 8 nodes between ADHD-C and ADHD-I types . There were no significant differences in regional nodal degree between ADHD types or controls after correcting for multiple comparisons . Differences at an uncorrected threshold comparing the two ADHD types ; associations identified at the uncorrected level are summarized in We performed a correlation analysis using the significant network edges revealed in the whole brain NBS analysis between the ADHD-C type and controls and the ADHD-RS clinical measures. None of the network connections significantly correlated with ADHD symptom severity  and inattentive (ADHD-I) presentation types of ADHD using NBS and graph-theoretical metrics. NBS analysis of the whole-brain connectome did not identify significant differences between the ADHD types or the ADHD-I type compared to controls. We identified a functional connectomic signature characterizing ADHD-C participants from controls, with primarily reduced intra-network connectivity within the DMN; and inter-network connectivity between the DMN and somatomotor (SSN) and limbic networks; and somatomotor (SSN) and cingulo-frontoparietal (CFP), ventral, and dorsal attention networks. Within network connectivity analysis of the DMN also revealed connectivity differences between the ADHD-C and ADHD-I groups. However, these connectivity differences were observed in the context of no differences in global or regional network properties between the two ADHD types, utilizing graph-theoretical analysis. Additionally, within network connectivity of the DMN and ADHD-RS hyperactive-impulsive symptom subscale scores were negatively correlated.Previous resting-state fMRI studies have demonstrated impaired connectivity in large-scale functional brain networks in ADHD, distinguishing ADHD types from each other . In addiThe present study found decreased inter-network connectivity related to the somatomotor network, CFP and DMN brain networks in ADHD-C relative to controls. The regulation of executive function, attention and response inhibition, which clinically characterize ADHD types , is propWhile within network connectivity for the DMN distinguished ADHD-C from controls, reduced connectivity in the network also characterized ADHD-C from ADHD-I type. Specifically, we found reduced inter-nodal anterior-posterior connectivity of the DMN and in posterior regions of the DMN. These regions assist regulation of delayed gratification in social decision making and emotional response, planning, and sensorimotor control . FurtherThis finding supports previous studies demonstrating distinct functional network connectivity mostly in the DMN and somatomotor, characterizing ADHD-C from ADHD-I  and alsoReduced connectivity between the three networks of the DMN and somatomotor network interacting with the CFP may be concordant with failure to suppress the DMN, effectively compromising the ability of the cognitive control network to activate and apply to attenuation of the task and motivation . MoreoveThe correlation results confirmed the relationship between aberrant functional connectivity within the DMN and ADHD symptoms in the two ADHD types. Items 10–18 on the ADHD-RS correspond to the hyperactive-impulsive DSM symptom criteria. Significant negative associations were found between connectivity within the DMN, linking the ventral mPFC and right lateral parietal gyrus, ventral mPFC and right parahippocampus, and right parahippocampus and cerebellar tonsils, with hyperactive-impulsive subscale scores. Interestingly, these regions underpin decision making, self-regulation, memory and response inhibition, with the cerebellar tonsils known to play a role in visual-motor and perception, characteristic of ADHD-C clinical symptoms and proposed DMN and visual and motor networks implicated particularly to ADHD-C type . SimilarThis study has several limitations. First, considering the small sample size of our study, the generalizability of results is limited; thus, replication in larger cohorts is warranted to deduce whether large-scale functional networks differ between the ADHD types, especially at the whole connectome level. We have provided uncorrected findings in the Additionally, medication effects and stimulant treatment history may bias our results despite participants undergoing a washout phase before study commencement. It is challenging to eliminate previous treatment effects, which reportedly “normalize” brain maturation by reducing atypical higher resting-state functional network connectivity . Thus, wIn summation, our study used intrinsic functional connectomic analyses incorporating network and graph-theoretic analyses underlying the two most common clinical ADHD presentation types, ADHD-C and ADHD-I. The new insights this study provides into functional intrinsic network phenotypic differences between the two types revealed within network differences in the DMN; and large scale brain inter-regional network differences between the somatomotor cingulo-frontoparietal and default mode networks, in between ADHD-Combined and controls. Thus, contributing to the burgeoning evidence of large-scale brain network topology in ADHD types, our findings potentiate distinct neurobiological features underlying the ADHD types in line with the literature. Furthermore, understanding the neural network architecture underlying the ADHD types provides evidence of differential network connectivity between the ADHD symptom presentations. This has significant implications for developing differential treatments suited for one type versus another.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by the Western Sydney Local Health District Human Research Ethics Committee. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.MSK, LW, MRK, SC, and KG contributed to the participant recruitment, clinical assessment, MRI data collection, and processing and management. JFS performed the data analysis and interpretation of results, with contributions from TB and MSK, and contributed to preparing and writing the original manuscript draft. JFS, MSK, LW, KG, and TB contributed to the review and editing of the manuscript. All authors contributed to the article and approved the submitted version.JFS, KG, MRK, and SC have received honoraria for educational seminars from Takeda . LW has received consultant fees from BlackThorn Therapeutics and scientific advisory board fees from Psyberguide of the One Mind Institute. MSK, KG, MRK, and SC have received commercial funding from Takeda for funded project grants. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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In this study, we sought to determine the nature of the abnormality in resting-state default mode network (DMN) activation and explore its correlation with functional connectivity in attention-deficit/hyperactivity disorder (ADHD). We obtained resting-state functional magnetic resonance images of youth with ADHD and typically developing counterparts from the publicly available ADHD-200 database. We used data from Peking University (232 scans) and New York University (172 scans); the scan repetition time was 2 s for both data collection sites. We applied generalized estimating equations to estimate the variability of the averaged blood-oxygen-level-dependent (BOLD) time series extracted from the DMN at rest. We performed network-based statistics to determine the association between the observed differences in BOLD signal variability and altered functional connectivity. We analyzed data from 105 youth with ADHD  and 140 typically developing youth , who aged 7–17 years. The imaging data were cross-sectionally collected for each participant at one time point. We observed a greater number of significant BOLD signal changes and higher-order polynomial significant associations in youth with ADHD. Moreover, there were significant between-group differences in BOLD signal change after the first 140 s, which coincided with decreased resting-state functional connectivity within the DMN in youth with ADHD. Increased variability of neural signaling was intermittently observed in the brains of youth with ADHD at rest, thereby indicating their default mode state was more unstable than that of typically developing youth. The default mode network (DMN) is a collection of distributed brain regions that are synchronously activated during rest or internally oriented mental activities –3, such Individuals with attention-deficit/hyperactivity disorder (ADHD) have difficulties in maintaining focus on tasks and are easily distracted by unrelated thoughts , 11. An per se. Therefore, we aimed to identify abnormalities in resting-state DMN activation and determine their association with strength  or stability . We also explored whether the nature of the abnormality is continuous  or intermittent . Moreover, we aimed to compare the variability , key elements of DMN characteristics in individuals with ADHD, to that of typically developing counterparts. The integrity of activation of task-positive networks during a task gets interrupted by an aberrant spontaneous activation of the DMN in ADHD  studies in individuals with ADHD reporting on decreased connectivity within the DMN , few stu in ADHD , 14. SimWe further hypothesized that aberrant DMN activation may introduce greater variability in individuals with ADHD. Only a few studies have previously tested similar hypotheses. In individuals with no current or past history of psychiatric diagnosis, the blood-oxygen-level-dependent (BOLD) signal variability in rs-fMRI data decreases linearly over the lifespan  in the majority of large-scale networks. Therefore, a decrease in the resting-state signal variability may be a component of the normative trajectories of functional brain maturation . ADHD isa priori hypothesis stated that BOLD signals would be less stable or destabilized earlier in youth with ADHD. Specifically, we expected to observe a series of significant changes in BOLD signals across time. First, a greater number of significant changes will be found in youth with ADHD, thereby suggesting an increased variability of neural signaling in their brains at rest. Second, significant changes will be observed earlier in these individuals while they have trouble in maintaining the default mode state of their brains. Third, we also anticipated a greater number of higher-order  polynomial significant associations in individuals with an unstable default mode state.Generalized estimating equations (GEEs) are a widely used statistical approach for modeling longitudinal data. Considering BOLD signal time series as a type of longitudinal data that are repeatedly measured at brief intervals of a few seconds, we applied GEEs to explore the changing pattern of BOLD signals in the brains of youth with and without ADHD. The In summary, we aimed to identify whether aberrant DMN activation introduces greater variability in individuals with ADHD. We expected to observe brief intermittent nature of abnormal resting-state neural signaling in youth with ADHD and their difficulty in maintaining the integrity of default mode state at rest. Accordingly, for example, no significant difference was likely to be found between the diagnostic and control groups during the first few minutes of scanning; however, the difference in BOLD signal change would be evident in a later phase of scanning, thus supporting the intermittent nature of abnormal DMN activation in individuals with ADHD. After exploring the variability of resting-state BOLD signal changes, we further explored the correlation between the observed instability of DMN activation and decreased functional connectivity within the DMN of youth with ADHD .http://fcon_1000.projects.nitrc.org/indi/adhd200/) and ADHD-200 Preprocessed repository (http://preprocessed-connectomes-project.org/adhd200/). The complete dataset consisted of MRI scans and phenotypic information collected from eight international sites. To ameliorate concerns about different scanners and scanning parameters among the collection sites, we aimed to reduce the number of data collection sites but retain maximum participants. Considering our purpose to detect instability and/or intermittency of BOLD time series, we prioritized rs-fMRI data with greater measurements , in addition to a larger sample size. Consequently, we used data from Peking University (232 scans) and New York University (172 scans), the two largest data collection sites in the ADHD-200 dataset. For participants whose brain images were collected more than once, we used images from the first data collection.We obtained the study data from the publicly available ADHD-200 database  above 80 were required to participate. The severity of ADHD symptoms were assessed using the Attention-Deficit/Hyperactivity Disorder Rating Scale-IV (ADHD-RS-IV) and the Conners' Parent Rating Scale-Revised, Long version (CPRS-R-LV) at Peking University and New York University, respectively. Participants withheld psychostimulants at least 48 and 24 h before scanning at Peking University and New York University, respectively.The ADHD-200 data include in-house quality assessment results for both structural MRI and rs-fMRI scans, which are binary data of either “pass” or “questionable.” We included youth with “pass” for both structural MRI and rs-fMRI scans and no missing IQ scores. Head motion during scanning is another critical concern in rs-fMRI studies. Based on the 10 head motion parameters provided in the ADHD-200 data, we labeled values over 2.2 interquartile range units below the lower or above the upper quartile as outliers . We exclWe used the preprocessed rs-fMRI data released by the Neuro Bureau ADHD-200 Preprocessed repository. Images were preprocessed using the Athena pipeline . We obtat-tests and chi-square tests for continuous and categorical variables, respectively. We applied GEEs to the averaged BOLD time series from the DMN. Due to the correlated structure of data  from repeated measurements, we used GEE, which assumes neither a normal distribution nor independent data. The scan repetition time was 2 s for both data collection sites. To match the number of measurements, we discarded some of the later scans from Peking University and analyzed 172 scans for both New York University and Peking University. To achieve our study objectives, we carried out two sets of GEE analyses, with three models in each set of analyses. Considering numerous measurements, we did not expect the entire time series to fit a specific  model; thus, we split the entire time series into small units of five scans (or 10 s) each and subsequently applied a GEE to each unit.We estimated between-group differences in descriptive statistics and head motion parameters using Student's First, we separately analyzed youth with ADHD and healthy controls to explore changes in the averaged BOLD signals from the DMN across time in each group. We tested three models, namely, linear, quadratic, and cubic, where we consecutively added the z-transformed time (zTime), zTime squared, and zTime cubed to the models, along with sex and age. The time variable was the sequential order of the scanning time points, ranging from 1 to 172. The dependent variable was the resting-state BOLD signal.Second, statistically significant differences in BOLD signal change between the ADHD and control groups were tested by combining the groups and entering the interaction terms between diagnosis and time variables. The GEE model 1 included age, sex, diagnosis, zTime, and the interaction of diagnosis × zTime, whereas we added zTime squared and the interaction of diagnosis × zTime squared in model 2, and zTime cubed and the interaction of diagnosis × zTime cubed in model 3. The dependent variable was the resting-state BOLD signal.t-tests. Using Pearson's correlation coefficient, we separately determined the association between ADHD symptom severity and MSSD for Peking University and New York University samples. This is because different measures of ADHD symptoms were adopted by the two data collection sites. We performed statistical tests, including the GEE analyses, using SPSS , and reported the results with a significance threshold of uncorrected P < 0.05. In addition, since the entire time series were split into small units and the tests were repeated for each unit, we applied a false discovery rate (FDR) correction for multiple comparisons.We also tested an alternative measure of signal variability, the MSSD , 27, 28.a priori cluster-defining threshold of three and shared nodes in common into a cluster of suprathreshold edges. Subsequently, we calculated family-wise error (FWE) probabilities for the number of connections in the cluster through 10,000 permutation tests. We included sex, IQ, and one  of the 10 head motion parameters as covariates owing to their significant difference between the groups. Age was not included as a covariate, given the absence of a between-group difference. We performed these steps using the NBS software package (http://www.nitrc.org/projects/nbs/) and visualized the results using BrainNet Viewer (http://www.nitrc.org/projects/bnv/)  .There were no significant differences in age and data collection sites between youth with ADHD and controls . The groa priori  in youth with ADHD as well as in typically developing controls, regardless of MSSD calculated for the entire time series, the first 70 scans, or the next 70 scans. When the Pearson correlation analyses were performed separately among female and male participants, no significant correlations were observed.P < 0.05, 10,000 permutations; Considering the significant differences identified from the GEE analyses after the first 70 scans, we separately applied NBS to the first 70 scans and the next 70 scans, thereby predicting a significant difference in the functional network only in the next set of scans. The NBS analysis did not reveal significant between-group differences in the first 70 scans, whereas a network with significantly decreased connectivity was observed within the DMN of youth with ADHD using the next 70 scans  polynomial significant associations were observed in youth with ADHD, thus suggesting the possibility of increased moment-to-moment BOLD variability in their DMN at rest. In addition, we identified significantly abnormal resting-state DMN activation in these individuals. As predicted a priori, the nature of the aforementioned abnormality was intermittent rather than continuous. Moreover, significant differences were principally observed in the interactions between diagnosis and time variables rather than the diagnosis per se, thereby suggesting the core feature of the abnormality lies in the variability of BOLD fluctuation over time, and not in the strength of BOLD activation. It is important to note that the between-group differences were not observed in the overall analyses, but only in the later scans, indicating a certain degree of abnormality in the maintenance of the resting-state DMN activation. In summary, increased variability of neural signaling was observed in the brains of youth with ADHD at rest, which indicated their default mode state was more unstable than that of typically developing youth.We observed intermittent changes in resting-state BOLD signal fluctuation in youth with and without ADHD. As expected Changes in the earliest scans in the control group were unexpected and indicated a supposedly more rapid BOLD stabilization in youth with ADHD at the beginning of rest, despite no between-group difference in the combined analysis. It is unclear if this finding reflected another core feature of ADHD  or the need to discard more initial scans before the analysis. The preprocessing of the Athena pipeline removed a relatively small number of the first four volumes, compared with other studies.We also observed decreased resting-state functional connectivity within the DMN in youth with ADHD, consistent with previous findings . HoweverAcross the 91 DMN regions of interest included in the study, 28 regions were involved in the network with significantly decreased connectivity in youth with ADHD. Resting-state functional connectivity is measured based on the correlations of brain activity across different brain areas and may thus be closely related to the moment-to-moment changing pattern of neural signaling in each of these areas. Further work is required in order to better understand the relationship between BOLD signal variability and functional connectivity.In this study, we examined a novel approach for exploring moment-to-moment BOLD signal changes. The GEE is far from being novel; however, it has been rarely applied in neuroimaging research. Contrarily, MSSD is one of the most commonly used measures of resting-state brain signal variability and has been investigated in ADHD , ASD 2121, and sThis study had some limitations. First, youth with and without ADHD were not strictly matched for sex and IQ. Considering our primary purpose was to separately explore the variability of BOLD fluctuation in youth with ADHD and typically developing youth, we did not perform case-by-case matching, which would reduce the sample size. Instead, we opted to maximize our sample size in each group as well as maintain the typical characteristics of the ADHD population , such asIn conclusion, this study indicated that the variability in resting-state BOLD fluctuation increased in the DMN of youth with ADHD, particularly in the later phase of scanning. Increased variability was intermittently observed and related to decreased functional connectivity within the DMN, compared with typically developing youth. To the best of our knowledge, this is the first study to apply GEE to investigate the changing patterns of resting-state BOLD signals. Further studies are required on the variable and intermittent nature of aberrant resting-state neural signaling in individuals with ADHD and their difficulty in maintaining the default-mode state.http://fcon_1000.projects.nitrc.org/indi/adhd200/) and Preprocessed repository (http://preprocessed-connectomes-project.org/adhd200/).The data used in this study are publicly available from the ADHD-200 database (This study used deidentified data acquired from an open data repository and was granted exemption from review by the Institutional Review Board at the Seoul National University Hospital.S-BH was responsible for the study concept and design, downloaded and analyzed the data, and drafted the manuscript. SH assisted with data analysis and interpretation of findings, and provided critical revision of the manuscript for important intellectual content. Both authors critically reviewed content and approved final version 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.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.


Level of Difficulty: Advanced.)Balloon entrapment is a potentially fatal complication of percutaneous coronary intervention. This report describes the use of subintimal plaque modification for the management of entrapped balloons. This technique, commonly done during chronic total occlusion angioplasty, was used successfully to retrieve the balloon. (  A nuclear medicine scan demonstrated moderate ischemia of the inferior wall. A CAG performed via a right radial artery demonstrated a severely calcified stenosis on the proximal and mid right coronary artery (RCA), with a calcific nodule . The lefThe patient had no significant cardiac or extracardiac medical history.We attempted to remove the balloon by forceful traction, but the distal marker started lengthening as we pulled further. We were able to partially pull the balloon into the guiding catheter, but neither the balloon nor the wire could be removed further. A balloon fragment was still present in the mid RCA on angiography. The patient developed chest pain at this point and the antegrade flow was TIMI flow grade 1.We cut the balloon shaft and advanced a 5.5-F Guideliner (Teleflex), with deep intubation of our guiding catheter, but the guiding extension was unable to advance past the balloon fragment in the mid RCA. Further, directed pulling through the guide extension catheter did not allow the retrieval of the entrapped balloon. Subsequently, we obtained a right femoral artery access to advance another 6-F Judkins Right guiding catheter to perform the Ping-Pong techniqueA guidewire was used to access the subintimal space proximal to the entrapped gear, followed by a microcatheter Turnpike LP (Teleflex) and was advanced beyond the entrapped balloon. An attempt to reenter the true lumen was performed at this stage with a Gaia 3 Next wire (Asahi Intecc), but failed. A Gladius Mongo 14 (Asahi Intecc) was advanced into the subintimal space at the mid vessel, and a subintimal tracking and reentry was attempted but was also unsuccessful , Video 4We thought of using a String-ray balloon (Boston Scientific), yet we wanted to avoid the use contrast, which would have caused large proximal dissection planes. Subintimal plaque modification and external plaque crush were the last remaining options to dislodge the entrapped balloon. We predilated the subintimal space with a 2.0- and 2.5-mm noncompliant balloon to 16 atmospheres , Video 5Intravascular ultrasound was performed using the Refinity system (Philips) and demonstrated a subintimal hematoma distally, a well-expanded and well-apposed stent, and circumferential calcium in the mid and proximal RCA, with a calcium nodule at the site of the balloon rupture and entrapment. The final CAG showed TIMI flow grade 3 antegrade in the RCA , Video 6In this report, we describe the use of a subintimal plaque modification technique by side-ballooning the subintimal space, which is the first time this bailout strategy was undertaken to manage this rare complication. The incidence of entrapped material during PCI is reported at a rate between 0.4% and 1.0%, and the incidence of balloon entrapment is even lower.However, despite this rare occurrence, entrapment of a balloon is a potentially fatal complication and must be managed urgently. We reviewed the literature by searching PubMed for occurrences of balloon entrapment using the keywords “balloon entrapment,” “balloon rupture,” and “undeflatable balloon” and found 21 cases highlighting this complication . Use of The patient was discharged 3 days later, after an uneventful postprocedural course. Peak troponin T Hs level was reached the next day (659 ng/L).The subintimal plaque modification technique is a bailout strategy for balloon entrapment and should be kept in mind in these critical situations.Dr Avram is supported by the Fonds de Recherche du Québec en Santé ; and has received speaker fees from Servier Canada, Boehringer Ingelheim and the Quebec Cardiologist Association. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.


Maternal education, income-to-needs ratio, and family history of psychopathology, defined class membership more strongly than child factors. An almost identical LCA structure from Study 1 was replicated in Study 2 . Indices of sensitivity  and specificity  were high across studies. It is concluded that the classifications represent valid combinations of child, parent, and family characteristics that are predictive of ADHD in young children.We aimed to identify subgroups of young children with differential risks for ADHD, and cross-validate these subgroups with an independent sample of children. All children in Study 1 (N = 120) underwent psychological assessments and were diagnosed with ADHD before age 7. Latent class analysis (LCA) classified children into risk subgroups. Study 2 (N = 168) included an independent sample of children under age 7. A predictive model from Study 1 was applied to Study 2. The latent class analyses in Study 1 indicated preference of a 3-class solution (BIC = 3807.70,  Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurobiological disorder characterized by inattention, hyperactivity and/or impulsivity. Children diagnosed with ADHD have severe negative outcomes compared to children without ADHD in terms of physical and mental health comorbidity, family and peer difficulties, substance use, academic problems, mortality rates and psychosocial adversity ,5,6,7,8.The diagnostic stability of ADHD diagnosis from preschool to middle childhood within community samples is approximately 50% ,19,20,21While these studies have examined predictors of ADHD diagnostic stability, they have relied on testing a priori hypotheses. In contrast, person-centered analytic approaches are able to model clinical population heterogeneity to identify novel subgroups of children who are at disproportionately higher risk for more or less favorable outcomes. Thus, one way to approach this is to use person-centered or latent class analysis, which is a non-parametric variant of cluster analysis that postulates a discrete latent variable to classify individuals with similar symptoms into more homogenous subgroups. This kind of analysis has been used to identify subtypes of ADHD in the past, specifically classifying children based on parent-reported symptoms, child/adolescent reported symptoms, personality, comorbidity, neuropsychological profiles, and trajectory of symptoms ,40,41,42In a most relevant recent study  the authThe above study profiled individuals with ADHD to identify the presence of meaningful subgroups of individuals and found predictive validity for ADHD stability using these subgroups. While this study was an important first step in identifying pathways through which ADHD symptoms became persistent from early to middle childhood, it was possible that classes reflected idiosyncratic sample-specific patterns. Since latent class analysis was a data-driven approach to identifying subgroups, sample characteristics could have a profound effect on the groups identified. As the previous study was on an ADHD sample from a tertiary care clinic, it might also represent children with more severe impairments from ADHD and not the entire population of children with ADHD during early childhood. Importantly, the relevance of these subgroups to the wider population of individuals with ADHD could only be determined through replication.In Study 1, we examined the percentage of ADHD diagnostic stability from preschool to adolescence in a tertiary care program that diagnosed each child’s preschool ADHD diagnosis with neuropsychological testing. In Study 2, we utilized more comprehensive parent and family socioeconomic variables to establish subgroups of children with differential ADHD risk and cross-validate the presence of these clusters in a second, unrelated sample of preschool children from the community.Participants were 120 children (21 girls and 99 boys) between the ages of 3-years, 0-month and 6-years, 11-months with a mean age of 5-years, 7-months (S.D. = 11.35 months) from the Boston metropolitan area in the USA. These children underwent assessments by a multidisciplinary team comprised of pediatric psychologists and developmental-behavioral pediatricians and were consecutively diagnosed with ADHD from 2003 to 2008.After a mean follow-up interval of 7 years later, confirmation of ADHD was conducted using the Diagnostic Interview Schedule for Children-4th Edition (DISC-IV), a structured parent interview, the Parent and Teacher Vanderbilt ADHD Rating Scales, and the Parent Global Assessment scale (PGA), a 7-point scale ranging from 1 (no impairments) to 7 (needs 24-h supervision due to severe impairments) ,46. ADHDParticipants were 168 children (54 girls and 114 boys) between the ages of 3-years, 0-month and 6-years, 11-months with a mean age of 5-years, 8-months (S.D. = 14.51 months) again recruited from the Boston metropolitan area in the USA. Eighty-four children were diagnosed ADHD using the DISC-IV (Young Children Version), Child Behavior Checklist (CBCL) , and theIn Study 1 and 2, we measured the same constructs and included these identical constructs in the latent class analysis. Replication of our observations from Study 1 in an independent sample (Study 2) constituted a full and independent replication of our observations. In addition to directly replicating our findings in two samples, we also tested the predictive validity of Study 1 findings through developing a set of predictive equations regarding latent class membership and testing these equations with Study 2 data. For example, we tested whether the children with and without ADHD from Study 2 were aligned correctly to their latent classes using predictive equations developed in Study 1. This allowed us to determine whether equations developed in a sample (Study 1) could be used to obtain predicted membership probabilities for a second sample (Study 2).In performing a latent class analysis, one concern is identifying the optimum numbers of clusters. We address this using both theoretical and methodological means . TheoretThe analytic strategy employed in Study 1 was replicated in Study 2 with one important addition . BesidesOur proposed model was simulated for the presence of 3 latent classes and membership coefficients in logits ranging between −3 and +1 distances. Using 1000 replications of sample sizes equal to 100 participants results indicated that entropy values (accuracy of classifications based on model parameters) were equal to 95.1%. The model was slightly overpowered with regard to the Likelihood Ratio Chi-square test  with estimates of AIC and BIC being also very close to 95% . These estimates, however, can be markedly different compared to observed estimates, for which proper values were unavailable given absence in the literature of relevant studies.Data on specific conditions related to parental psychopathology were available from Study 1 data only. These results are presented here for descriptive purposes only as sample size restricted further predictions using specific conditions of parental psychopathology. Nevertheless, in Study 1, there was a high prevalence of parental history of ADHD (53.3%), anxiety/depression (43.9%), a learning disability , and a bipolar disorder (25.8%).F = 0.274, p = 0.602]; Study 2: F = 0.828, p = 0.364], Income-to-Needs Ratio , salary , parental psychopathology , externalizing symptoms , internalizing symptoms , and parental education .For gender differences inferential statistics contrasted boys and girls on either level (means) or frequency of a categorical variable . Results pointed consistently to the absence of gender differences. Specifically, there were no differences in levels of poverty [Study 1: p < 0.001) and the descriptive fit indices . The number of children per class were 38 (Class 1: High ADHD probability), 35 , and 13 (Class 3: Very low ADHD probability). Classification errors were equal to 0.0042%. Given those numbers it was considered reasonable to not pursue additional classes, as the number of cases would significantly reduce and would likely invalidate the generality of the emerged solution. Initially a 1-class latent mixture model was applied to the data in order to provide a baseline on which other models were compared, as this 1-class model did not have the presence of subgroups. The first comparison involved the fit from a 2-class to the baseline model. When the combination of child, parent, and family variables were modeled, results indicated that the 2-class structure fit the data from Study 1 better compared to a 1-class solution . IndicesStandard2 = 99%; Rentropy2 = 98%). As shown in This modeling approach utilized information from the latent class analysis of Study 1 to develop a series of predictive equations from that dataset. We then applied those predictive equations to the data in Study 2 and evaluated how well the known membership was aligned with those from the predictive model. Agreement between predicted and actual membership scores regarding ADHD and non-ADHD membership was tested using sensitivity and specificity analysis. As shown in The purpose of the present studies was to identify and confirm the presence of subgroups of young children with ADHD as a function of child, parent, and family characteristics. We found an identical three-class solution in Study 1 and the data were replicated using an independent sample of children in Study 2. In both studies, we observed that three groups of children with ADHD emerged-one group who was socioeconomically advantaged and had a low likelihood of carrying an ADHD diagnosis and two groups of more disadvantaged youths who were more likely to meet criteria for ADHD diagnosis.The present studies were designed to add to the extant literature in a number of ways. To our knowledge, previous studies examined how child-specific predictors, such as neuropsychological deficits, group together within individuals, and did not include parent and family predictors. This was the first person-centered analysis to investigate how child, parent, and family variables combined across individuals. This analysis differentiated young children with and without ADHD into subgroups, and these subgroups were cross validated in an independent sample with high sensitivity and specificity.2 = 0.512, 0.886, and 0.831, respectively, indicating that children who came from disadvantaged backgrounds tended to belong in the classes with higher probabilities of ADHD.Importantly, this study found that parent and family factors defined class membership more strongly than child factors. Maternal education, household income, and income-to-needs ratio were the most discriminating variables in the prediction of ADHD in young children with RIt has been well documented that parents who are under-resourced have higher rates of adversity exposures, increased exposure to stress, and lower parental involvement, all of which are likely to increase risks for psychopathology ,64,65,66Our findings supported the complex interplay of both child and family factors in the susceptibility of ADHD. While twin, family, and adoption studies have demonstrated a strong heredity component in ADHD symptoms with a twin heritability estimate of ADHD of 0.77 ,40,69, mIn summary, clinicians should consider family SES when recommending and implementing treatment for young children with ADHD, as children in low SES families may be the most vulnerable. When family risk factors are present, clinicians should consider interventions that target the home environment as well as the child’s specific behaviors, .The present study was limited for several reasons. First, the sample size of Study 1 was modest and would allow for idiosyncrasies in the observed latent class membership. However, the stability with which the latent class structure observed in both Study 1 and 2 data was very high. Furthermore, the application of the predictive model of Study 1 latent structure with Study 2 data was associated with high rates of accurate classification of ADHD cases and non-ADHD cases. Thus, adoption of the present methodology and the use of cross-validation remarkably lowered the possibility that the present findings were idiosyncratic and non-representative of the population.With a modest sample size, this study did not include all environmental exposures in early childhood, including measures of violence exposure, prenatal exposures, nutrition, psychosocial supports of families, and parental stress. Additionally, this study was unable to disentangle many interrelated variables such as SES and increased genetic predisposition. The variable of “parental history of psychopathology” captured genetic disposition of ADHD of a child, but at the same time, could be an indicator of suboptimal parenting. These interactions were difficult to tease out due to the relatively small sample size where a large number of parent psychopathology predictors would be suboptimal from the statistical point of view. However, anecdotally, there was a high prevalence of anxiety/depression, ADHD, and a learning disability in the parents of the children. Furthermore, our coding scheme in Study 2 did not specifically record each and every one of parent psychopathologies but instead utilized a binary system for the presence of any psychopathology. Certainly, a larger scale, prospective study is needed to further pursue these goals.In the future, this study may be extended to include a follow-up of children in Study 2, in order to identify and cross-validate subgroups of children who are at the highest probability of persistent ADHD over time. This kind of LCA will have utility in predicting future trajectories of individuals with ADHD and will allow clinicians to decipher those who are at greatest risk of ADHD persistence from early to middle childhood, ensuring that the limited resources available for intervention are provided for the children most in need of them.In conclusion, this study points to the important environmental and home influences on the susceptibility of ADHD. To further expand our understanding of ADHD heterogeneity and its current classification, employment of additional variables related to genetic, physiological, academic neurocognitive measures, and neuroimaging findings are warranted in future studies.


Attention-Deficit/Hyperactivity Disorder (ADHD) was classically considered a childhood-onset neurodevelopmental condition. Over the past 40 years, it became evident that it can persist during adulthood.The purpose of the authors is to describe the characteristics of ADHD in adults and the specific comorbidities, proposing an approach to these patients.A brief non-systematized review is presented, using the literature available on PubMed and Google Scholar.Only 40-50% of children and adolescents with ADHD will have symptoms that persist into adulthood . A more subtle presentation in adults and the difficulty to access past medical history, lead to diagnosis and treatment rates of lower than 20% (versus 50% in children). Well-characterized core symptoms in children evolve into a predominance of inattention symptoms. They became adults with marked disorganization, difficulties in completing tasks and managing time. Emotional dysregulation is a very prevalent symptom in this population. The comorbidities rate increase over time (reaching 75% of patients).Adults (or even older subjects) with cognitive and/or behavioural complaints should be submitted to systematic screening for ADHD. Non-treated ADHD symptoms in adulthood are associated with severe impairment, therefore adjustments in the health care system to support the transition from child to adult services are needed.No significant relationships.


Performance on executive function (EF) tasks is only modestly predictive of a diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), despite the common assumption that EF deficits are ubiquitous to the disorder. The current study sought to determine whether ex-Gaussian parameters of simple reaction time are better able to discriminate between children and adults with and without ADHD, compared with traditional measures of inhibitory control.N = 108), middle childhood (N = 309), and young adulthood (N = 133).Receiver Operating Characteristic (ROC) analyses and the area under the curve (AUC) were used to examine the ability of performance on two commonly used tasks of inhibitory control  and go-no-go tasks) to predict ADHD status in preschool  time to “go” trials, all successfully discriminated between individuals with and without ADHD. Ex-Gaussian decomposition of the RT distribution indicated that both larger tau and larger sigma drove findings for SDRT. Contrary to predictions, traditional measures of inhibitory control were equal if not better predictors of ADHD status than ex-Gaussian parameters.Findings support ongoing work to quantify the separate contributions of cognitive subprocesses that drive task performance, which in turn is critical to developing and improving process-based approaches in clinical assessment. Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties sustaining attention, hyperactivity, and impulsivity . Much ofAlthough genuine cognitive heterogeneity within the broad phenotype of ADHD likely exists , it may tau and standard deviation of RT were highly diagnostic of ADHD (both AUC = .96), mu and sigma held much lower diagnostic value (both AUC = .62)  across a range of reaction time tasks in middle childhood . In addiC = .62) .Like the ex-Gaussian parameters, alternative mathematical methods such as the diffusion model  and linevs. non-ADHD) is predicted by data obtained during the course of an evaluation. Due to their ability to efficiently quantify the sensitivity and specificity of a decision-making procedure, and their ability to allow for the direct comparison of the predictive utility of different tests, ROC/AUC analyses are now widely taught in graduate assessment courses to aide in clinical decision-making  in the prediction of ADHD. We further predict that tau will outperform SDRT and sigma. However, disinhibition is central to mechanistic theories of ADHD . Thus, tData for this study was collected within larger programs of research aimed to better understand the cognitive mechanisms that contribute to the execution of complex behaviors, including those associated with psychopathology and ADHD. Three separate samples were recruited for the study: “preschool” (5–6 years) , “schoolChildren were excluded if parents reported any sensorimotor disability, frank neurological disorder (e.g. seizures), autism, psychosis, or if limited proficiency in English would impair full participation. Adults were excluded for the abovementioned reasons, as well as for a history of concussion that resulted in a loss of consciousness for more than 10 min.Participants with low estimated Full-Scale IQ were excluded from all studies  or WAIS-IV (adult) samples). Other common psychiatric disorders, including anxiety, depression, oppositional defiant disorder, and conduct disorder, were assessed using parental report on the DISC-IV or self-report using the Adult Self-Report Scale , but werNpreschool = 1, Nschool aged = 83, Nyoung adult = 42). Participants prescribed a non-stimulant medication were excluded from participation. However, one preschool participant was prescribed a non-stimulant medication (Strattera). Because it has a longer half-life, the child did not discontinue medication use.All participants prescribed a stimulant medication were asked to discontinue their use 24–48 h in advance (N = 75 (23 girls) identified as ADHD. In the school-aged sample, this resulted in an N = 216 (73 girls) identified as ADHD.Children were recruited from the Centre, York, and Harrisburg counties of Pennsylvania. Children were identified as having ADHD if they met full DSM criteria for a diagnosis of ADHD on the Diagnostic Interview Schedule for Children version IV (DISC-IV) , includiand one teacher report were required (a) to exceed a t-score of 61 (85th percentile) on ADHD-related indices of the BASC (Hyperactivity or Inattention) or Conner’s , or (b) have endorsed ≥ three inattentive or three hyperactive/impulsive symptoms, or ≥ four total symptoms, as often or very often on the ADHD Rating Scale.Following DSM field trials, final symptom count was derived following the “or” algorithm to integrate parent and teacher report: a symptom was counted as present if either the parent (on the DISC-IV) or the teacher (on the ADHD-RS) indicated that a symptom was present “often” or “very often” . As evidt-score ≤ 58), and <3 inattentive symptoms and <3 hyperactive/impulsive symptoms, and <4 total symptoms using the “or” algorithm. In the preschool sample, this resulted in an N = 33 (15 girls). In the school-aged sample, this resulted in an N = 93 (46 girls).Children were classified as non-ADHD control participants if they had never been diagnosed with or treated for ADHD in the past, parent and teacher reports on all above listed rating scales were below the 80th percentile  if they met full DSM criteria for a diagnosis of ADHD in adulthood. Following DSM, at least five symptoms of inattention or hyperactivity/impulsivity were required to be present. Cross-situational severity and impairment were determined by self-report on the Conners’ Adult ADHD Diagnostic Interview (CAADID) (Adults were identified as having ADHD ((CAADID) , Connors(CAADID) .N = 72, 30 women) if they had never been diagnosed with or treated for ADHD in the past and reported <2 symptoms of inattention or hyperactivity/impulsivity and <3 total symptoms on the CAADID.Adults were classified as non-ADHD control participants . Though non-ADHD controls had higher mean FSIQ than same-aged ADHD peers in both the preschool and adult samples , they were well within the population average.Validating the diagnostic strategy described above, there were significant differences in the number of inattentive and hyperactive symptoms across groups  were Go trials and 30%  were No-Go. Time to completion was approximately 6 min, including task instructions.Over 60 trials, children consecutively viewed one of four shapes . They were asked to make a key press when they saw a blue shape , but to withhold a response when they saw a red shape . Each shape appeared for 1000 ms, and children were given 2000 ms to respond. The next trial began immediately after a response or after 2000 ms had elapsed. A brief error tone was provided after incorrect responses. Of the trials, 70%  and contained 61–70 asterisks. The remaining 25% were “no-go” trials  and contained 31–40 asterisks. Children were not provided any instruction on how to distinguish “a lot” from “a little,” but were provided with a brief error tone after incorrect responses. Time to completion was approximately 20 min, including task instructions.Ten blocks of 80 trials were administered with optional rest periods in between. At the start of each trial, a number of white asterisks filled random positions in a 10 × 10 array centered on a black screen . Children = 150). The other 25% (n = 50) were “stop” trials in which an auditory tone was presented to indicate that children should not respond. Time to completion was approximately 20 min, including task instructions.Children were administered a 200-trial tracking version of the Logan Stopping Task that ensures a 50% failed inhibit rate for all participants . Five bl1. SSRT is defined as the amount of time a child needs to successfully inhibit a response 50% of the time. It was calculated by subtracting the mean stop signal delay from the child’s MRT.An initial mean reaction time (MRT) was determined based on 20 practice “go” trials presented prior to the start of the experimental blocks. The auditory stop tone was initially set to occur 250 ms before the MRT. The MRT was then dynamically recalculated after each correct go trial, and the delay at which the stop tone was presented was adjusted dynamically in 50 ms increments to maintain an overall ~50% accuracy raten = 300 trials) were “go” and 25% (n = 100) were “no go.” No feedback concerning accuracy was provided. Time to completion was approximately 15 min, including task instructions.Four blocks of 100 trials were administered, with 12.5 s of rest between each block. At the start of each trial, a central fixation point  appeared in the center of the screen for 500 ms against a black background. The bar then turned green, aqua, orange, yellow, or blue. Participants were asked to make a key press as quickly as possible  except if the bar was blue . Stimuli appeared onscreen for 750 ms during which responses could be collected. The next trial began after the 750 ms had elapsed. Of the trials, 75% , indicating poor fit, likely due to the relatively low number of trials. Due to strong fit for mu and sigma, we proceded with analyses for the preschool sample but address the implications of uncertain fit for tau in that sample in the Discussion.For all tasks, dependent variables include: mean and standard deviation as well as ex-Gaussian parameters  for “go” trials. In the SSRT task, SSRT was also a dependent variable. ROC analyses are commonly used to evaluate the sensitivity and specificity of test predictions . ROC anap < .05, all η2 > .046). There was no main effect of ADHD status for any sample in MRT to “go” trials, but SDRT was larger among children/adults with ADHD in all tasks and all samples . Examination of the ex-Gaussian parameters found sigma and tau to be significantly longer in the school-aged sample ; tau was also longer in the young adult sample .The diagnostic utility of each task parameter was assessed using ROC curves and evaluating the AUC see .p < .001). Percent failed inhibits in the preschool sample predicted ADHD in the correct direction, but was not significant .As expected, given their history in the study of ADHD, SSRT and % failed inhibits on a GNG task discriminated between ADHD and typically developing participants in both the school and adult samples . Ex-Gaussian parameters allow for the decomposition of the RT distribution to illuminate the source of this effect. In the school-aged sample, the ability of SDRT to do so was driven by both sigma and tau ; in the young adult sample, it was driven by tau . Sigma also discriminated between groups in the school-aged sample for both tasks . Decomposition of the RT distribution did not provide any additional information for the preschool sample.Of the performance parameters formed by simple reaction time to “go” trials, SDRT discriminated between diagnostic groups in all tasks and all samples . Z-scores representing differences in the strength of discriminability between any two variables were generated. Z-scores were created by using their AUC values, standard errors and the correlations between the two predictors within each group  See Tab. For thePreschool and school-aged children, as well as adults with ADHD, had slower stop signal reaction times (derived from the SSRT task), a greater percentage of failed inhibits (derived from a go-no-go task), and larger SDRT to “go” trials than those without ADHD. Ex-Gaussian decomposition of the RT distributions indicated that the findings for SDRT were driven by both sigma and tau in school-aged group and by tau in the adult sample.The health of multiple broad subprocesses necessary for successful task performance  can be inferred from the shape of the RT distribution . CompetiThe specific conceptual interpretation of tau adopted here is that tau represents the speed of information accumulation during the decision-making. This interpretation is strongly informed by a mathematical model of choice reaction time task performance known as the diffusion model . Though Findings for tau were not seen in the preschool sample, however. Preschool children with ADHD did not have longer taus than their non-ADHD counterparts. The lack of an ADHD-related group effect in this youngest group is likely representative of sample-specific methodological differences, rather than true developmental differences. Because of its influence on cognitive activation, the interstimulus interval is commonly used to experimentally manipulate the shape of RT distributions. RTs are slower and SDRTs are larger when interstimulus intervals are longer in typically developing children and children with ADHD . In the n = 60 total trials, 42 go-trials) influenced findings. Parameter recovery for mu, sigma, and tau for school-aged and young adults, as well as mu for the preschool sample, was all quite strong . However, the sigma and tau parameters for the preschool sample produced a simulated RT distribution that was significantly correlated with the empirical distribution (r = .4), but still below the commonly applied threshold (r = .8). Therefore, the lack of group effects for preschool tau could also be due to lower trial numbers producing less accurate estimates of tau, as opposed to suggesting that RT variability functions differently in young children with ADHD. Because data are best fit to a distributional model when there are many individual data points , it is aSSRT, % failed inhibit, SDRT, tau, and sigma (for the school-aged sample) all predicted the presence of ADHD. However, in direct head-to-head comparisons, SSRT was a much stronger predictor of ADHD status than any other metric, contrary to hypotheses. Furthermore, despite being a more specific index of variability at the tail of the distribution, tau did not outperform SDRT. That being said, each parameter independently predicted ADHD status to a degree that was equal to or stronger than those reported in studies of similar tasks that are currently and commonly used in clinical practice. For example, studies of the Conners’ CPT-II have reported AUC values for standard error ranging between .63 and .71, and for percentage of failed inhibits ranging between .43 and .67 . Of partBecause they are designed to reflect the signs and symptoms of disorder, AUC values for behavioral rating scales in the prediction of disorder are commonly quite large (ranging from .70s to .90s) due to that tautological advantage . PerformEffective screening tools are those that can be rapidly and inexpensively administered. They are designed to select a relatively low optimal cutoff to maximize the number of individuals warranting further evaluation, while tools used for ruling out a diagnosis tend to use higher cutoffs. Possible consequences of a “false positive” diagnosis of ADHD include social stigma associated with diagnosis, failure to make an appropriate alternative diagnosis, or even possible iatrogenic effects of an unnecessary medication . PossiblCognitive tasks such as those within the current study are likely not suited to functioning as screeners as they cannot be quickly and cheaply administered. Additionally, because a large portion of people meeting diagnostic criteria do not show impairments on cognitive tasks such as these, their use as a method to rule out a diagnosis would likely result in many missed diagnoses. The current work is consistent with process-based approaches in clinical assessment and is aligned with current efforts to develop and improve upon a new generation of neuropsychological tests to evaluate cognitive weaknesses and treatment response .While the same broad go-no-go paradigm was implemented with all three age groups, the specific tasks administered did vary in several ways, including type and number of stimuli, complexity, time between stimuli, and feedback regarding accuracy. Each task was designed to be developmentally appropriate in length and motivational demand requirements, but those design decisions also introduced sample specific confounds that may have led to variation in performance, rather than true age-related differences. In the preschool-aged task, specifically, the decision to design the task with fewer trials may have contributed to the lack of findings in that age group. It is recommended that future work continue to develop paradigms that are able to maintain motivation and active assent during participation, while also ensuring an adequate number of trials to ensure strong parameter recovery across age groups, as demonstrated by a parameter recovery study to ensure good fit to the data. Furthermore, future studies should aim for a more even gender balance, enabling careful evaluation of if and how male and female participants demonstrate genuinely different cognitive performance profiles, requiring separate normative values.better identify ADHD status than standard indices, these findings support ongoing work in developing and improving process-based approaches in clinical assessment.Across multiple developmental periods, traditional indices of inhibitory control as well as variance in the speed to “go” all successfully predicted ADHD status. While results did not support initial hopes that ex-Gaussian parameters of performance might Supplementary Material