Although there is some evidence for a normalization of brain structure following exposure to ADHD medication, literature on the effects of duration and dose of continued use on the brain is scarce. Here, we investigated the association between cumulative exposure to medication (range 1 week to 4.69 years) and cortical structures and subcortical volumes in a clinical sample of children with ADHD taking medication (n = 109). To the best of our knowledge, this is the first structural MRI study investigating the effects of cumulative exposure to medication on subregional volumes in children treated for ADHD.Cumulative exposure to ADHD medication (CEM) was defined as the product of duration on medication (days) and dose (mg/day), yielding the area under the curve . Cortical thickness and surface area measurements (CIVET-1.1.12), and subcortical volumes in 51 regions (MAGeT-Brain) were analyzed using general linear modelling.Significant effects of CEM were found in two subregions of the left hippocampus, the CA1  and the strata radiatum/lacunosum/moleculare . Specifically, higher CEM was associated with smaller volumes within these subregions. No effects of medication exposure were detected on cortical thickness or surface area.Although this study is cross-sectional, the results found within this sample of children show that prolonged ADHD medication use at higher doses is significantly associated with smaller hippocampus volumes in specific subregions. More research is required to determine whether these results are reproduced in other samples of children of ADHD, and further, whether these are beneficial or off-target effects of the medication. The onset of ADHD occurs during childhood with core symptoms consisting of inattention, hyperactivity, and impulsivity . UntreatSeveral studies have proposed that ADHD symptoms are associated with reduced activity in dopamine (DA) and norepinephrine (NE) systems . Changesmeta-analysis found that only 25–50% of published reports had reproducible results . One study reported that medicated children with ADHD did not significantly differ from control children with regards to cortical thickness. Though, a higher rate of cortical thinning was detected in unmedicated children with ADHD . An earltructure . Specifitructure . Other structure  and ASD tructure . Both sttructure  or ventrtructure . The onltructure . Taken ttructure .Although normalization of certain brain structures by ADHD medication has been proposed, three important caveats should be considered. First, most studies assign a categorical designation (naïve vs. medicated) to investigate ADHD medication effects on the brain. Therefore, the effects of duration and dose of ADHD medication on brain structure in chronically-treated children have seldom been investigated. Indeed, neither of the two longitudinal studies nor the one prospective longitudinal study assessing the effects of ADHD medication on brain structure considered the duration or dose of medication use . TherefoSecond, although the abovementioned studies report no medication effects on the brain regions investigated, other studies have reported hippocampus volume reductions in adults with ADHD who had, during childhood, been treated with ADHD medication . These fThird, studies have documented the impact of motion in MRI research and have issued a note for caution when interpreting findings. Specifically, head and breathing motions during MRI acquisition led to underestimation of brain structure measurements . Since cThe goal of this study is to examine the effects of cumulative exposure to ADHD medication (duration × dose) on cortical and subcortical brain structures in a clinical sample of children being treated for ADHD. The first objective is to determine the relationship between ADHD medication and cortical thickness and surface area. Since medication has been proposed in some studies to normalize brain structure measurements, we hypothesize that significant increases in mean cortical thickness and surface area are associated with cumulative exposure to ADHD medication (CEM). The second objective is to explore the effects of medication on the volume of 51 subregions within subcortical structures . To keep in line with the concept of normalization, we hypothesize that CEM is significantly correlated with subcortical volumes. To the best of our knowledge, this is the first structural MRI study investigating the effects of CEM (duration X dose) on 51 subregional volumes in a clinical sample of children with ADHD.22.1One-hundred and forty-four unrelated children aged between 6 and 12 years  were recruited for the MRI study at the ADHD clinic of the Douglas Mental Health University Institute (DMHUI) in Montreal. The research protocol was approved by the Research Ethics Board of DMHUI. The study was explained to parents who provided written consent. Children gave verbal assent. Parents completed a general information questionnaire to acquire demographic data. Sample characteristics are available in Out of the total sample (n = 144), 109 children had a confirmed diagnosis of ADHD based on a clinical evaluation by a psychiatrist according to Diagnostic and Statistical Manual of Mental Disorder version 4  criteria and corroborated with the Diagnostic Interview Schedule for Children (DISC) administered to parents . Parents2.2All children  were scanned on site at the Cerebral Imaging Center in a 3 T Siemens MRI scanner to acquire T1 and T2-weighted structural images. Scanning time consisted of two rounds of 9 min. The protocol was tailored to a pediatric population to reduce motion during scanning. All children practiced on a mock scanner prior to MRI scanning, a cartoon was shown, sandbags were placed over their extremities and scans were repeated when necessary. Protocol details are described elsewhere .2.3https://github.com/CobraLab/minc-bpipe-library). Pre-processed scans were input to CIVET-1.1.12 and MAGeT-Brain for cortical and subcortical analysis, respectively. CIVET is an automated imaging software tool used to obtain corticometrics  . Pre-processing of raw scans was conducted to minimize downstream failures via the minc-bpipe-library pipeline ( Canada) . Cortica Canada) . The hip Canada) . MAGeT p Canada) . A final2.4Lifetime pharmacological history of ADHD medication was collected retrospectively as reported by the parents, and subsequently corroborated against pharmacy prescription logs. All children with ADHD participating in the MRI study were exposed to medication for a minimum of one week prior to scanning . Medication breaks  were considered and subtracted from the total duration from the date of initial exposure to date of scanning. ADHD medications were prescribed by the treating psychiatrist at different doses for various durations depending on the clinical needs of the child. For each period of treatment at a given dose, the exposure to medication was calculated as the product of duration and dose. The cumulative exposure to ADHD medication (CEM) was then calculated by summing all the exposures . 2.52.5.1https://github.com/Mouse-Imaging-Centre/RMINC/). Age and sex were stated as covariates, and cortical thickness and surface area as main outcome measures. Similarly, a GLM was generated for analysis of 51 subcortical volumes and CEM, where age, sex and total brain volume were included as covariates, and subcortical volumes as main outcome measures. To address potential cofounds between CEM and ADHD severity, a supplemental analysis using the Conners’ Global Index scale (CGI), which is a measure of the child’s ADHD symptom severity at baseline was performed. Further, to explore the effect of medication on brain structure in relation to age, an interaction analysis between age and CEM was performed. A supplemental non-linear model  was tested to explore the possibility of curvilinear effects of CEM on brain structure. Multiple-testing correction using FDR was performed.CEM data was log-transformed to generate a normal distribution  and RMINmeta-analysis associating them to ADHD dimensions. Four outcome measures  were selected based on a m to ADHD. Correct2.5.2All analyses regarding CEM were conducted in a clinical sample of children being treated for ADHD, as typically-developing children are unmedicated. To explore whether the regions significantly associated with CEM were independent of ADHD diagnosis or age, two complimentary analyses using a control group (n = 35) were conducted. Subcortical volumes within 51 subregions were compared between cases and controls. Demographics were assessed between groups and measures that significantly differed were included as covariates in the model . Specifically, age, sex, total brain volume, IQ and CEM were used as covariates, and each subcortical volume as the main outcome measure. Moreover, an interaction analysis between age and diagnosis was performed. Since a control group of typically-developing children exposed to ADHD medication is not ethically feasible, a 3-way interaction  was not possible to examine.33.1Five children with ADHD were concurrently prescribed anti-psychotics and were excluded from the final analysis (ADHD n = 101). The number of independent prescriptions for ADHD medication per child was one (n = 7), two (n = 34), three (n = 21), four (n = 18) and five (n = 21). A total of 315 prescriptions were included: Ritalin® (35.2%), Biphentin® (32.4%), Concerta® (22.6%), Vyvanse® (5.7%), Strattera® (2.5%) and Adderall® (1.6%).3.2No global effects of CEM on cortical thickness or surface area were detected on either hemisphere (n = 101). CEM did not significantly predict cortical thickness and surface area measurements in the vertex-wise comparison at the set FDR range. Likewise, no effects of CEM on cortical structures were observed when controlling for ADHD severity.3.3Significant effects of CEM were found in 2 out of 5 subregions of the hippocampus, the left Cornu Ammonis 1  and the left strata radiatum/lacunosum/moleculare (SR/SL/SM) .The right CA1 , right SR/SL/SM , right dentate gyrus  and left CA2/3  did not reach significance. Specifically, higher CEM was associated with decreased volumes within significant subregions . Post-hoSupplemental analyses controlling handedness and dosage equivalencies between medication types yielded the same findings . Results remained significant after controlling for ADHD symptom severity  as well as when controlling for exposure to the non-psychostimulant, atomoxetine (Strattera®) . Controlling for weight did not alter results. Moreover, the interaction analysis between CEM and age yielded no significant findings, and no significant effects were detected between hippocampus CA1 volumes and neuropsychiatric assessments (CPT performance). Finally, no non-linear effects of CEM were detected on the volumes of the 51 subregions included in our model.3.4Age, income, ethnicity and handedness did not significantly differ between ADHD and control children. Significant group differences were found for sex and full-scale IQ, and thus were included as covariates in the analysis. As expected, significant differences in ADHD symptomatology and behavioural measures were detected between ADHD and control children  and dose (mg/day), on cortical structures and subcortical volumes in medicated children with ADHD. To the best of our knowledge, this is the first neuroimaging study exploring the effects of CEM on 51 subcortical volumes in a sample of children all undergoing pharmacological treatment for ADHD. Significantly smaller volumes were found in subregions of the left hippocampus CA1 and SR/SL/SM and were associated with higher CEM. No significant medication effects were detected on cortical thickness or surface area.meta-analysis combining over 3000 scans from multiple sites reported smaller global hippocampus volumes in children with ADHD  and tested for association with CEM. Three supplemental analyses were conducted to help extrapolate medication effects: 1) case-control group comparison, and interaction analyses of 2) age-by-diagnosis and 3) age-by-CEM. Volumes within hippocampus subregions did not significantly differ between cases and controls in our sample, hinting that current findings were not unduly influenced by diagnostic effects. Previous case-control imaging studies that conveyed differences in hippocampus volumes used case groups either consisting solely of treatment-naïve ADHD children, or mixed exposure  . All thePharmacological studies conducted in rodents have found that psychostimulants increased synaptic levels of DA and NE in several brain regions, notably the prefrontal cortex, a region robustly associated with ADHD, and the hippocampus . ChangesNevertheless, the studies highlighted above employed non-ADHD animal models with typical catecholamine functioning. Since the current model for ADHD pathophysiology consists of DA/NE dysregulation, medication is likely to differently impact the brain of ADHD individuals . ConvergAlthough it is uncertain if deficits in neurogenesis and long-term memory are involved in ADHD pathophysiology, the hippocampus is also involved in motivation and emotional regulation, which are functions impaired in ADHD individuals. Although the underlying cause for the volumetric reductions in the present study cannot be confirmed, it is reasonable to hypothesize, given the considerable evidence from animal studies, that alterations in neurogenesis and synaptic modelling following chronic exposure to ADHD medication are driving the structural changes observed. In addition, no significant associations with omissions, commissions, variability, and reaction-time CPT scores were detected, suggesting that selective and sustained attention was not related to the CA1 volume reduction in our sample. However, further research is required to determine whether these volumetric decreases are directly associated with long-term memory function, motivation, emotional regulation, or other behaviours mediated by the hippocampus in humans.The major strength of this study is the detailed variable created for CEM. The structured interview with the parents in conjunction with access to the child’s prescription history provides the information required to generate a precise and quantitative value of medication exposure, which considers dose, duration, multiple prescriptions, and medication breaks. External validation of parental reports was also made possible through prescription pharmacy logs. The third strength is the specificity of the subcortical regions under study, which were subdivided into 51 regions to explore more localized effects, notably in the hippocampus. Moreover, restricting the sample to medicated children shifted the focus from comparing groups of treatment-naïve to treatment-exposed children with ADHD, to investigating medication effects within continuously-treated children with ADHD. This enabled the possibility of examining the association between various cumulative exposure points and subregional volumes, instead of assessing the effects initial exposure to medication between groups. Another strength is that all ADHD children were undergoing treatment for ADHD, which helped diminish motion during scanning and yielded viable scans that passed quality control . Furthermore, all scans were carried out on the same scanner located on site at the DMHUI, removing multi-site error.The present study should be viewed in light of its limitations. First, the cross-sectional design prevented the assessment of medication effects over time on brain development. Longitudinal studies investigating brain development as a function of CEM are warranted. Differences in brain structure are prominent in children, and while the effects of age were accounted for in the model, our sample was limited to a pediatric population limiting the generalizability of findings to other age groups. Similarly, sex was included in the model, though it is plausible that medication effects on brain structure are divergent in boys and girls. Previous studies in humans  and in a5The therapeutic response of ADHD medication and associated side-effects on behaviour are well documented in the literature. However, the effects of prolonged ADHD medication use and dosage on human brain structure remain elusive. This knowledge gap is relevant to researchers and healthcare providers, and raises important concern for individuals taking ADHD medication and parents of children with ADHD. Here, it was found that higher CEM was associated with smaller hippocampus volumes in the CA1 and SR/SL/SM subregions in medicated children with ADHD, and that these effects were independent of ADHD severity, sex, and age. Despite extensive research, neuroimaging studies in ADHD have garnered contradictory and irreproducible findings. This may be partly attributable to unaccounted medication effects on brain structure and head movements during scanning. Therefore, our results suggest that the effects of CEM should be considered in future ADHD neuroimaging research. Furthermore, awareness of the structural consequences induced by medication on the hippocampus sheds light on the pathophysiology of ADHD and may influence the decision-making process of ADHD treatment in children. Although smaller hippocampus subregional volumes were not associated with the cognitive dimensions tested in our sample, previous research has shown that smaller hippocampus volumes are associated with increased vulnerability to brain disorders later in life, memory deficits, sensitivity to trauma and anti-depressant resistance. Therefore, understanding the effects of CEM on the hippocampus may be an important factor in determining the optimal duration and dosage of treatment to avoid negative life outcomes, which may in turn instigate revaluation of current ADHD prescription practices. In summary, we report on a thorough approach to quantify cumulative medication effects and show smaller subcortical volumes are associated with higher cumulative exposure. This effect remained even when accounting for age and severity of symptoms. Although these findings are interesting, they should be interpreted with caution given the study’s limited cross-sectional design.Nellie H. Fotopoulos: Conceptualization, Investigation, Methodology, Formal analysis, Visualization, Writing - review & editing. Gabriel A. Devenyi: Methodology, Software, Data curation, Resources. Stephanie Guay: Data curation. Sarojini M. Sengupta: Supervision. M. Mallar Chakravarty: Resources, Software. Natalie Grizenko: Resources, Funding acquisition. Sherif Karama: Supervision, Validation, Methodology, Writing - review & editing. Ridha Joober: Supervision, Resources, Funding acquisition, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


KIF11, WAC, and CRBN. Then, we focused on common variants and identified six genes associated with ADHD : MANBA, UQCC2, HIVEP2, FOPX1, KANSL1, and AUH. Furthermore, HIVEP2, FOXP1, and KANSL1 were nominally associated with autism spectrum disorder (ASD) , as well as HIVEP2 with anxiety , and FOXP1 with aggression , which is in line with the symptomatology of the rare disorders they are responsible for. In conclusion, inspecting Mendelian disorders and the genes responsible for them constitutes a valuable approach for identifying new risk genes and the mechanisms of complex disorders.Attention-deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder characterized by hyperactivity, impulsivity, and/or inattention, which are symptoms also observed in many rare genetic disorders. We searched for genes involved in Mendelian disorders presenting with ADHD symptoms in the Online Mendelian Inheritance in Man (OMIM) database, to curate a list of new candidate risk genes for ADHD. We explored the enrichment of functions and pathways in this gene list, and tested whether rare or common variants in these genes are associated with ADHD or with its comorbidities. We identified 139 genes, causal for 137 rare disorders, mainly related to neurodevelopmental and brain function. Most of these Mendelian disorders also present with other psychiatric traits that are often comorbid with ADHD. Using whole exome sequencing (WES) data from 668 ADHD cases, we found rare variants associated with the dimension of the severity of inattention symptoms in three genes:  Attention-deficit hyperactivity disorder (ADHD) is characterized by symptoms of hyperactivity, increased impulsivity, and/or inattention, affecting approximately 5% of children and adolescents and 2.5% of adults worldwide . There iThe contribution of genetic factors to ADHD is estimated to be around 70–80% in children and adults . ADHD syhttps://www.omim.org/). Around 7000 rare diseases have been described, affecting 350 million people globally, and often including a wide range of symptoms. The vast majority are characterized by Mendelian inheritance, and most of them (80%) have a known genetic cause [Some known rare Mendelian diseases, such as Phenylketonurya, Adrenoleukodystrophy, Tuberous sclerosis, Fragile X, Muchopolysacaridosis type III, Hyperlysinemia, or Joubert syndrome, include symptoms of hyperactivity and/or inattention. These disorders also present with other psychiatric symptoms that are comorbid with ADHD, as described in the Online Mendelian Inheritance in Man (OMIM) database  with the search term “hyperactivity OR hyperactive OR attention OR inattention”. We did not include a search for the presence of impulsivity only, since this is frequently present in many disorders related to aggression and not hyperactivity. Therefore, we included impulsivity only when it is presented with hyperactivity, to avoid the inclusion of rare disorders with symptomatology not directly related to ADHD.We queried the OMIM database  [This search returned 182 OMIM entries that were manually curated to discard 18 entries not related to ADHD, as well as 27 entries without a known associated gene, ending up with 137 OMIM entries linked to 139 genes. From those entries, we retrieved information on related psychiatric and neurological disorders . From a http://www.impactadhdgenomics.com) and is described in more detail by Rovira et al. [To assess the contribution of rare variants in the OMIM gene list, we analyzed the presence of high-impact rare variants within those genes and their associations with ADHD symptom dimensions in a sample of adult patients with ADHD. The dataset comprised 668 German adults with persistent ADHD, diagnosed according to DSM-IV criteria; the mean age was 34 (ranging from 18–65), with 50.8% of the sample being males. This sample is part of the International Multicenter persistent ADHD Collaboration  or a 10% False Discovery Rate (FDR). As WES analyses were not performed for variants in the X chromosome, only genes located in the autosomes could be analyzed.Using a gene-based approach, we analyzed the cumulative effect of high-impact rare variants in the OMIM-derived genes with ADHD symptoms dimensions (inattention and hyperactivity/impulsivity symptoms). The statistical analyses were performed using the Multi-marker Analysis of GenoMic Annotation (MAGMA v1.07) software, with a SNP-wise mean model for the gene-based association analysis, adjusting for genomic principal components as covariates. We considered the genes to be associated if they reached the Bonferroni-corrected adjusted threshold for the number of genes tested  and 10% FDR.The contribution of common variants (MAF > 0.01) in the OMIM gene list to ADHD was assessed through a gene-based association analysis using MAGMA v1.06  and the https://www.med.unc.edu/pgc/download-results/, accessed on 10 April 2019), except for impulsive personality traits, where results from the gene-based association analyses were obtained directly from the published manuscript . As a result, we obtained a list of 139 genes responsible for 137 OMIM phenotypes containing at least one ADHD trait; there were 65 genes related to hyperactivity, 18 to inattention, and 56 to both symptoms . From thWe further annotated whether these 137 disorders also presented with other psychiatric or neurological comorbidities, frequently observed in ADHD patients, such as autism or aggressive behaviour. From the list of 139 genes known to cause these disorders, 57 genes were related to disorders that also present with symptoms of ASD, 49 with aggression, 15 with anxiety, and 14 with OCD . Other pWe tested the possible contribution of rare variants to hyperactivity/impulsivity symptom dimensions and to inattention symptoms in a clinical sample of 668 adults with persistent ADHD. From the original list of 139 OMIM genes, we were able to extract the set of rare variants from 111 autosomal genes, with 101 of them presenting at least one high-impact rare variant .KIF11, WAC, and CRBN), were significantly associated with inattention symptoms in this sample, with inattention being present in the Mendelian disorders they are responsible for  for the enrichment of rare variants in the permutation-based analysis.Subsequently, we used a permutation procedure to assess if the burden of high-impact variants identified in the OMIM gene list was higher than expected due to chance. We identified 667 high-impact variants in 103 OMIM-genes. We observed a trend close to statistical significance  and one with a short attention span (AUH) .We analyzed the contribution of common genetic variants in the OMIM gene list to ADHD, using a gene-based approach on the largest GWAS meta-analysis of the disorder, which included 19,099 cases and 34,194 controls [an (AUH) . HIVEP2 MANBA is differentially expressed in the cerebellum  and in the cerebellar hemisphere . It is important to note, however, that the information for most gene–tissue combinations was not available. We also inspected if common genetic risk variants in these six genes were associated with changes in subcortical brain volumes, using the summary statistics of a GWAS meta-analysis of seven MRI volumetric measures from the ENIGMA Consortium [HIVEP2 were associated with changes in the nucleus accumbens (p = 4.87 × 10−3), and AUH and MANBA in the pallidum . We also identified nominal associations for the MANBA gene with subcortical volumes of the hippocampus (p = 0.032) and the thalamus (p = 0.045).We explored whether the six genes associated with ADHD were predicted to be differentially expressed in cases versus controls in different brain areas, using MetaXcan . We obsensortium . We founFollowing the previously identified ADHD-associated genes, we observed that all the Mendelian disorders with hyperactivity symptoms also presented other psychiatric comorbid traits such as ASD, aggression, anxiety, impulsivity, or OCD . We inspHIVEP2, FOXP1, and KANSL1 using a GWAS of 18,382 cases and 27,969 controls . The gene UQCC2 did not show association with ASD.For ASD we found associations with the genes controls . AssociaHIVEP2 gene using a GWAS of 7016 cases and 14,745 controls  when combining ADHD with the comorbid trait in a gene-based meta-analysis . We did KIF11, that was significantly associated with ADHD in the analysis of rare variants, was also nominally associated with aggression, consistently with the symptomatology in this Mendelian disorder  and six carrying common genetic variants that are associated with ADHD . Furthermore, for HIVEP2, FOXP1, and KANSL1, which are causal genes for rare disorders presenting with psychiatric conditions that are frequently comorbid with ADHD, we also found associations with these comorbidities.In our study, we followed a new approach to identify novel risk genes for ADHD. We searched for Mendelian disorders with a known causal gene that present with hyperactivity and/or inattention among their symptomatology, and identified 137 OMIM phenotypes and 139 genes related to them. They are enriched in genes involved in neurological processes, brain function, and development. Nine of those genes were highlighted in our study, three of which showing association with rare variants related to the severity of ADHD symptoms (p = 1.85 × 10−13) [A total of 137 rare disorders with an ADHD-related phenotype were identified, representing 2.2% of the 6138 OMIM entries with a known molecular basis. A similar number (95 disorders) was found in a previous study investigating symptomatology featuring aggressive behavior in rare disorders . Among t× 10−13) . In rode× 10−13) . The fun× 10−13) .TBC1D2, was pointed to as a risk gene in a previous study in three families with multiple ADHD-affected members, with an apparent dominant inheritance pattern [Remarkably, one of the genes included in the OMIM gene list,  pattern . This geKIF11, WAC, and CRBN, which carry high-impact genetic variants associated with inattention severity. All disorders related to these genes include ADHD among their clinical manifestations: KIF11 is responsible for a disorder with microcephaly and lymphedema (OMIM #152950), WAC for Desanto-Shinawi syndrome (#616708), and CRBN is responsible for a recessive intellectual disability syndrome (#616708). The gene KIF11 encodes for a kinesin, CRBN, which is a protein involved in ubiquitination, and WAC, a molecular adaptor involved in ubiquitination and several biological processes such as the cell cycle and autophagy.The analysis of rare variants in the OMIM gene list pinpointed three genes, MANBA, UQCC2, HIVEP2, FOPX1, and KANSL1) were related to rare disorders presenting with hyperactivity, and one (AUH) with a short attention span. MANBA is responsible for β-mannosidosis (#248510), UQCC2 for Mitochondrial complex III deficiency (#615824), KANSL1 for Kooln-de Vries Syndrome (#610443), AUH for 3-methylglutaconic aciduria type I (#250950), and both HIVEP2 and FOXP1 are related to syndromes with intellectual disability .Analysis of common variants highlighted six genes associated with ADHD, five of which (MANBA, encoding for β-mannosidase, has been found to be associated with ADHD at a gene-wide significant level in our study. The encoded protein is a lysosomal enzyme that has been especially related to kidney disease but also to psychiatric disorders. Previous studies identified MANBA as a gene that is associated with schizophrenia and nicotine dependence [pendence , as wellpendence .HIVEP2, FOXP1, and KANSL1 have been found to be associated with ADHD and also were nominally associated with ASD in our study. HIVEP2 and FOXP1 code for transcription factors and both have previously been related to autism [HIVEP2 has also been linked to schizophrenia and substance use disorders [FOXP1 was also found to be associated with aggression in our study, but, to our knowledge, it has not been previously related to aggressive behavior. One of the loci associated with ADHD in the largest GWAS meta-analysis pointed to FOXP2, another member of the same family [FOXP2 forms homo- and/or heterodimers with FOXP1 and FOXP4 [o autism ,35,36. Hisorders ,38,39, aisorders ,41, whicnd FOXP4 ,43, and nd FOXP4 . Thus, tSeveral limitations of this study should be taken into account. On one hand, the sample size for the rare variant analysis is limited, and this could have prevented us from identifying associations of other genes with the dimension of the severity of ADHD symptoms. On the other hand, the statistical power of the GWAS meta-analysis, used for identifying common variants, is still limited, especially for OCD and impulsive personality traits, in which we could not identify any gene associated, potentially due to the limited sample size of these studies. Also, common variants could not be investigated separately in the different symptom domains of ADHD. Causal genes for Mendelian disorders presenting only with impulsivity, and not in combination with hyperactivity, were not investigated, which could have hindered our ability to identify additional relevant genes for ADHD. Finally, rare and common variants within genes located on non-autosomal chromosomes could not be assessed in this study. Therefore, further studies would be of help to confirm the role of these OMIM genes in ADHD.Overall, our results delineate an alternative way to identify candidate genes for complex disorders through exploring the shared phenotypic space with Mendelian diseases. We have seen that genes related to hyperactivity and/or inattention symptoms in Mendelian rare disorders could be relevant in the common and complex forms of ADHD, and have identified mechanisms that are potentially involved in neurodevelopment and brain function. Following this strategy, we have identified nine novel candidate genes for ADHD, three of them being of special interest for their potential pleiotropic role in several ADHD comorbidities.


Background: Patients with Internet gaming disorder (IGD) and attention-deficit/hyperactivity disorder (ADHD) have high comorbidity but it is still unknown whether these disorders have shared and distinctive neuroimage alterations.Objective: The aim of this meta-analysis was to identify shared and disorder-specific structural, functional, and multimodal abnormalities between IGD and ADHD.Methods: A systematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies comparing people with IGD or ADHD with healthy controls. Regional gray matter volume (GMV) and fMRI differences were compared over the patient groups and then a quantitative comparison was performed to find abnormalities (relative to controls) between IGD and ADHD using seed-based d mapping meta-analytic methods.Result: The meta-analysis contained 14 IGD VBM studies (contrasts covering 333 IGDs and 335 HCs), 26 ADHD VBM studies , 30 IGD fMRI studies (603 patients with IGD and 564 controls), and 29 ADHD fMRI studies (878 patients with ADHD and 803 controls). Structurally, VBM analysis showed disorder-specific GMV abnormality in the putamen among IGD subjects and orbitofrontal cortex in ADHD and shared GMV in the prefrontal cortex. Functionally, fMRI analysis discovered that IGD-differentiating increased activation in the precuneus and shared abnormal activation in anterior cingulate cortex, insular, and striatum.Conclusion: IGD and ADHD have shared and special structural and functional alterations. IGD has disorder-differentiating structural alterations in the putamen and ADHD has alterations in the orbitofrontal cortex. Disorder-differentiating fMRI activations were predominantly observed in the precuneus among IGD subjects and shared impairing function connection was in the rewards circuit . Internet gaming disorder (IGD) is characterized by difficulties in controlling online gaming behaviors, including symptoms such as craving , 2, lossSeveral comprehensive reviews reported a strong correlation between IGD and ADHD . These tCurrent evidence shows that most addictive diseases exert initial reinforcing effects by activating reward circuits in the brain . WeinsteIn addition, Blum et al.  showed tThe above studies showed brain structural abnormalities were observed in the cingulate, striatum, frontal, and temporal lobes between these two disorders , 17. MorAccording to previous studies, we hypothesized that disorder-specific GMV abnormality would be shown in the OFC among ADHD subjects  and in tSystematic and comprehensive searches were performed in the PubMed, Web of Knowledge, and Science Direct databases from January 1, 2010, to October 31, 2020, using different combinations of the keywords “voxel-based morphometry” or “VBM” or “morphometry” or “gray matter” or “functional magnetic resonance imaging” or “fMRI” and “online-game” or “Internet gaming disorder” or “IGD” or “Attention-Deficit/Hyperactivity Disorder” or “ADHD.” We identified further papers by reference tracking and consulting retrieved high-quality meta-analysis and review articles.The included studies had to meet the following criteria: (1) they provided whole-brain pairwise voxel-based comparisons of patient groups (IGD or ADHD) relative to controls; (2) they were a task-related fMRI or VBM study; (3) they provided peak coordinates in Montreal Neurological Institute (MNI) or Talairach spaces; (4) the diagnosis of ADHD patients had to be based on DSM-IV-TR, or DSM-5, or ICD-10 criteria, and IGD was diagnosed according to DSM-5 or YIAS or CIAS; and (5) there were no neurological or psychiatric comorbidities .We excluded studies that had fewer than 10 patients, those that used only ROI analyses, duplicated patient data, or no eligible contrasts , 30–32. The two authors (Gao and Zhang) assessed all articles and achieved 100% agreement.https://www.sdmproject.com/), following MOOSE guidelines for meta-analyses of observational studies. The AES-SDM data processing procedure is briefly summarized here . AES-SDM uses an anisotropic non-normalized Gaussian kernel to recreate an effect-size map and an effect-size variance map for the contrast between patients and controls from peak coordinates and effect sizes for each VBM or fMRI study. Coordinates were converted to Montreal Neurological Institute (MNI) space for this analysis. Following this, a mean map is created by performing a voxel-wise calculation of the random-effects mean of the study maps, weighted by sample size and variance of each study and between-study heterogeneity. In addition, full width at half maximum (FWHM) was set to 20 mm because this setting was optimal to balance sensitivity and specificity and other parameters included voxel P = 0.005, peak height Z = 1, and cluster extent = 10 voxels (We used an anisotropic effect-size version of the Seed-based d Mapping software package (AES-SDM) (version 5.15) to conduct the voxel-wise meta-analysis  relative to controls and then between the two disorders. Second, fMRI meta-analyses were conducted to examine the neural activation abnormalities observed within and between disorders using all available data. Then a conjunction analysis across both patient groups relative to controls was further performed to examine areas of shared/contrasting abnormalities; This conjunction method was also used within patient groups to conduct multimodal analyses, which showed regions of overlapping functional and structural abnormalities compared with controls. Some studies used multiple task contrasts, several functional tasks, or identical controls. Combined maps with reduced variance were calculated to avoid dependent data in the analyses . To examp < 0.005 was used for all meta-analyses , 26 ADHD VBM studies , 30 IGD fMRI studies (603 patients with IGD and 564 controls), and 29 ADHD fMRI studies (878 patients with ADHD and 803 controls). See z = −1.155; P = 0.248), and Chi-squared test showed both groups contained a significantly greater proportion of males . In the fMRI meta-analysis, patient groups did not differ in age  but a large proportion of patients with IGD and ADHD were males . Age and sex were consequently included as covariates in all between-group meta-analyses performed including only the adult studies, which were age and sex matched.In the VBM analysis, Wilcoxon W tests revealed that patient groups did not differ in age  in the bilateral anterior cingulate cortex (ACC), median cingulate cortex (MCC), superior frontal gyrus (SFG), the supplementary motor cortex (SMA), right putamen/striatum, bilateral inferior frontal gyrus (IFG), and left middle frontal gyrus (MFG) .ADHD Patients compared with HCs showed significantly lower GMV in the bilateral ACC/olfactory cortex/median cingulate, bilateral striatum, left caudate nucleus, left precentral gyrus/postcentral gyrus, right superior frontal gyrus, and orbitofrontal cortex (OFC) .People with IGD, relative to ADHD, had more reduced GMV in the right striatum ; while people with ADHD showed lower left caudate nucleus GMV , relative to IGD .Across all fMRI studies, people with IGD showed activation in the bilateral precuneus/cingulate cortex (CC), right OFC, left angular gyrus/middle temporal gyrus (MTG)/MOG, left precentral gyrus, bilateral IFG, right caudate nucleus. Moreover, IGDs had lower activation in the right precentral and postcentral gyri, right insular/rolandic operculum, compared with controls .Patients with ADHD relative to controls showed overactivation in the right DLPFC, right MFG, and right ACC. Hypoactivation was observed in the right precentral gyrus (motor cortex), left STG/insula/OFC, right STG/MTG, left DLPFC/MFG .ADHD was associated with disorder-specific hypoactivation relative to IGD in the L MCC, R MTG, R caudate nucleus, and L MFG .In patients with IGD, decreased GMV and functional connection relative to controls overlapped in the right insular/putamen  while increased GMV overlapped with increased activation in the right angular gyrus/MOG and precuneus . The left ACC and right IFG was decreased in volume and increased in function connection in patients with IGD relative to controls  .As for ADHD patients, increased GMV and functional activation relative to controls overlapped in the right fusiform gyrus  while decreased GMV overlapped with decreased activation in the right superior temporal gyrus, left inferior frontal gyrus, and left postcentral gyrus  .P > 0.05 for all comparisons, Bonferroni corrected), suggesting that there was no publication bias. Jack-knife reliability analyses suggested robust disorder-differentiating findings.Egger's tests were performed to examine potential publication bias. The results of the Egger tests were non-significant  and insula (decreased GMV and lower activation). In addition, striatum, expecting abnormal structure in both two disorders, was reported to be reduced in GMV and functional connectivity in the IGD group and reduced in GMV and no significant change in fMRI for the ADHD group.The key disorder-shared abnormality in two disorders both in structure and function is the prefrontal-striatum circuit. The circuit network contains the anterior cingulate cortex, the orbital prefrontal cortex, the ventral striatum, the ventral pallidum, the dorsal prefrontal cortex, amygdala, hippocampus . AttentiAs predicted, the results of the main meta-analyses converged on the ACC, which showed functional hyperactivation and gray-matter reduction in IGDs and ADHD relative to HCs. This finding is in line with recent transdiagnostic meta-analyses that this region may serve as a common bio-marker across psychiatric disorders , possiblThe disorder-contrasting findings in OFC are worth discussion. Now it is commonly understood that the OFC contributes to psychotic dysfunction including impulse control and monitoring ongoing behavior and rewards-seeking behaviors . Our decAs for the striatum, which comprises the caudate nucleus and the putamen, the nucleus accumbens (NAc), and the olfactory tubercle, which appear in our result. When IGD or ADHD patients are exposed to cue-relative stimulation, the activation of glutamatergic projections from the ventral PFC, the ventral hippocampus, and the amygdala  to striatal projections that increase DA signaling and release in the NAc and dorsal striatum will enhance reward craving and eventually result in game activity in IGD and distraction in ADHD . HoweverWe found consistent changes in the insula, which had decreased GMV and lower activity in ADHD and IGD subjects. The insula are involved in motivation, rewards, salience detection, and cognitive control , 135, moWe find decreased putamen GMV in IGD people through VBM analysis. The dorsolateral putamen has been functionally linked to the sensorimotor cortices, forming the sensorimotor network. A recent research report that health controls show a significant positive correlation in the neural pathways connecting the putamen-MFG-insula when facing gaming cues, which is missing in individuals with IGD . MeanwhiIn our study, disorder-specific activation is suggested in the precuneus among IGD patients. The precuneus is associated with visual imagery, attention, and memory retrieval by participating in the visual process and integrates related memory . A possiThis meta-analysis has several limitations. First, it was based primarily on peak coordinates rather than raw statistical brain maps. Besides, the heterogeneity of the methodologies among VBM studies could not be avoided, such as the differences in MRI machines, slice thickness, preprocessing protocols , and smoothing kernel size, which might have contributed to the inconsistent results . MoreoveThe comparative meta-analytic findings of this study stress the shared and distinctive brain structure and function in IGD and ADHD. Disorder-differentiating structure alterations are reported in the putamen for IGD and in the orbitofrontal cortex for ADHD subjects. Disorder-differentiating fMRI activation was predominantly observed in the precuneus among IGD subjects. The shared functional alterations focus on the frontal-striatum reward circuit, which is important for understanding the underlying pathophysiology and proves that these two disorders have a common neurological foundation. Disorder-shared neurofunctional biomarkers provide useful evidence that the drugs treat ADHD could be used on IGD. Disorder-specific neurofunctional biomarkers could ultimately aid in the development of future, disorder-differentiated behavioral, pharmacological, or neurotherapeutic treatments.The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.XG and YZ designed the experiment. XG, MZ, and ZY performed the experiment. MW, HH, RZ, WW, YW, JC, SH, and YZ modified the experiment and paper. All authors contributed to the article and approved the submitted version.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.


In this paper we provide new existence results for isoperimetric sets of large volume in Riemannian manifolds with nonnegative Ricci curvature and Euclidean volume growth. We find sufficient conditions for their existence in terms of the geometry at infinity of the manifold. As a byproduct we show that isoperimetric sets of big volume always exist on manifolds with nonnegative sectional curvature and Euclidean volume growth. Our method combines an asymptotic mass decomposition result for minimizing sequences, a sharp isoperimetric inequality on nonsmooth spaces, and the concavity property of the isoperimetric profile. The latter is new in the generality of noncollapsed manifolds with Ricci curvature bounded below. Given a Riemannian manifold isoperimetric problem consists in studying the minimization problemn in isoperimetric profile of isoperimetric set or isoperimetric region.For any entclass1pt{minimaThe problem of the existence of isoperimetric regions in the setting of noncompact manifolds is a hard problem that has seen several important progresses in the last years. Let us just mention some of the most important contributions in the field related to the topics of this work. Major results in the application of a direct method for proving existence of isoperimetric sets in manifolds with lower bounds on the Ricci curvature are contained in , 69. TheDifferential properties of the isoperimetric profile, some of which are generalized in the present paper, have been studied in –21, 65. Euclidean volume growth, i.e., such thatn-dimensional unit ball. The above limit exists as a consequence of the Bishop–Gromov monotonicity  if it is isometric as a metric measure space to Before stating our first result we recall that V in M.Let Recall that any sequence N possesses no asymptotic cones that split a line . In view of this we can, at least at a heuristic level, rephrase our statement as follows: isoperimetric regions of big volume exist provided the isoperimetric sets of the model spaces at infinity are uniformly stable.We point out that  has a cl. Hence,  simply sWe now state our last result regarding concavity properties of the isoperimetric profile. This will be an important ingredient in the proof of the previous existence theorems and has an independent interest. Given V of twice differentiability it holds the pointwise estimateSuppose that a priori, and, in fact, we will employ Theorem The previous result is obtained under the natural assumptions for the study of the isoperimetric problem, namely noncollapsedness and Ricci bounded below. In particular, differently from the classical results in , 63, 65,mentclass2pt{minimM by a sequence of penalized perimeters k. Also, the profile k.To this end, in order to prove Theorem }Pk, see , implies}Pk, see , convergAs a consequence of Theorem As anticipated, Theorem V, as a consequence of the lower semicontinuity of the perimeter. The main enemy for the existence of the limit is the possibility of having pieces of Broadly speaking the proof of Theorem N. On the other hand, if part of the mass of any minimizing sequence is lost at infinity along the factor The asymptotic decomposition result in  see The below tementclass2pt{minim setting , 44, 53.I and its derivative N, see Lemma To perform such a plan we need to know the exact asymptotic behaviour for large volumes of the isoperimetric profile I is the same as the isoperimetric profile of a cone with opening I is a direct consequence of Bishop–Gromov comparison theorem and the sharp isoperimetric inequality, while the computation of the asymptotics of Loosely speaking, the behaviour at infinity of We finally stress that the extremal case in the hypothesis in Theorem N splits a line, then the density of every point at distance 1 from the line of the tips of every asymptotic cone of M is uniformly bigger than M has density equal to C, which is an asymptotic cone of N, resulting in a contradiction. Second, by means of the volume convergence theorem, a lower bound on the density of points at distance 1 from the line of the tips of the asymptotic cones is readily seen to imply a lower bound on the N. It is worth pointing out the reference [Let us comment on the proof of Theorem eference , where tThe result in item (i) of Lemma While the assumptions in Theorem g has nonnegative Ricci curvature provided b and c are chosen big enough. It is easy to see that g is invariant under the translation along the direction not satisfied.It is possible to prove that any volume.On the other hand, along sequences All in all, we conclude that our assumptions in Theorem To date we do not know whether manifolds with nonnegative Ricci curvature and Euclidean volume growth always admit isoperimetric regions for big volumes, and we plan to investigate further this problem in the future.In Sect.  Theorem , and we  Theorem . Section Theorem .In this preliminary section we introduce basic facts and notations about Riemannian manifolds and metric measure spaces.n-dimensional Hausdorff measure g. This notion coincides with its classical Riemannian volume measure induced by g. For the notions of BV and Sobolev spaces on Riemannian manifolds we refer the reader to [E in E inside P(E). We denote with g. If E is a P(E) coincides with its classical area measure induced by g. We recall that for every finite perimeter set E one has E and Given two Riemannian manifolds We recall the following terminology.Convergence of finite perimeter sets) Let locally converges to a measurable set E if the characteristic functions  It can be proved, see [ the volume of the ball of radius r in the (unique) simply connected Riemannian manifold of sectional curvature K of dimension n, and by s the volume of the boundary of such a ball. In particular We denote by On metric measure space,for short,pointed metric measure space is a quadruple Let us now briefly recall the main concepts we will need from the theory of metric measure spaces. We recall that a X.For simplicity, and since it will always be our case, we will always assume that given Given two m.m.s. We assume the reader to be familiar with the notion of pointed measured Gromov–Hausdorff convergence, referring to .pGH and pmGH convergence) A sequence pointed Gromov–Hausdorff topology, in thesense for short, to a pointed metric space  We recall that, whenever it exists, a pmGH limit of a sequence  Here we recall the synthetic variant of Gromov’s precompactness theorem for ., 60.6, 6BVfunctions and perimeter on m.m.s.) Let bounded variation functionsslope of u at x, for any accumulation point  ofEinA byE has finite perimeter if If ely, see .isoperimetric profile ofE is an isoperimetric region.The It follows from classical approximation results  Let E with finite measure has finite perimeter if and only if the characteristic function , , 2.2), a(Bishop–Gromov comparison) Let r and the function r, i.e., (Bishop–Gromov comparison theorem on m.m.s.) Let us recall that for an arbitrary Moreover, if Let us also recall a classical definition for the convenience of the reader.and Euclidean volume growth) Let asymptotic volume ratio of Euclidean volume growth. when we want to higlight the fact that Z is the metric space that is the basis of the cone. We stress that every such C is an (Asymptotic cones) Let X is independent on the base point We refer to [t unique , 36, 71 In this section we recall the main generalized existence result of , which wgiven in  capitaligiven in .strong andconvergence) Let (ii)M;(iii)n-dimensional Hausdorff measure on for every (iv)it holds that (Asymptotic mass decomposition [We remark that item (ii) in Theorem We will need the following result on the boundedness of isoperimetric regions.)=V. By , we haveExplicit examples of collapsed manifolds with nonnegative Ricci curvature have been constructed in dimension In the same paper, it is shown that the latter examples do not exist in dimension Let If the conclusion does not hold, from Proposition The proof of the previous result Proposition Here, we provide a version of the sharp isoperimetric inequality in ained in  ained in  allows tE, with  spaces) Let r-enlargement of E, i.e. the set of those points The isoperimetric inequality [We will need the following elementary lemma proven in [Let f by using the decomposition in positive and negative part. Let us finally show  unit normal along In this section we derive first and second variations for the potential term in X is a smooth compactly supported vector field on M, then we can define the tangential divergenceE has generalized  mean curvature of Ein directionX on M. In such a case, the vector generalized  mean curvature ofE. Obviously, if Let us further recall that, if V. Then X on M, where X.Suppose that }Pk, see , at voluMoreover, X generates a volume preserving variation, i.e., whenever The first variation tisfying .Being The regularity of e recent   vanishes at some Now we can prove that r. Given such E as in  and  implies one with  with \doWe also need the following second variation formula for the potential term.M be a complete Riemannian manifold. Let Let X is normal along For some X is normal along The first variation  follows identity  follows In the hypotheses and notation of Lemma  so that  reads3.1We will need the following well known lemma that, roughly speaking, gives an approximation in Let Then for any The above lemma has been proved in the Euclidean setting in [In the following proof of Theorem For the ease of notation we do not explicitly write the volume forms under the integral sign, since it will be clear from the context.v(t) and J(v) are well posed, indeedt if v is locally invertible and J(v) is well defined for v(t). We thus haveI is continuous and strictly positive ( [I(V) is bounded below by a strictly positive constant independent of Let ly as in  and ,,\documenI is locally Passing to the limit as }k→+∞ in , taking }k→+∞ in . Finally}k→+∞ in  is known}k→+∞ in , see [19nce from  the ineq proving . \documeIt would be interesting to understand how the above proof could be improved in order to get concavity properties on the function udied in –21, 63.In this section we collect some consequences of Theorem Suppose that I under nonnegative Ricci curvature, together with the sharp isoperimetric inequality , there exists a ball of center p, radius V such thatFor simplicity, throughout the proof we denote st prove . We firsLet us now prove  when \dorison in . Since \nce from  also in M is not noncollapsed, then  in  contradiV, thus concluding the proof of the Theorem.Let us now conclude the proof of Theorem V of bounded finite perimeter sets. Let To reach the latter conclusion we start proving that there exists V for which, by contradiction, there exists exactly one M. Now, by exploiting item (iv) of Theorem Let us prove the previous claim by contradiction, after having provided the precise value of V, by using the same notation as before,Let us now consider the case =0. From  we have V in M. Indeed, let us take a minimizing sequence V. We have already shown that for every j’s since j since it is an isoperimetric region in M. Hence, by properly translating V.Let us now take er, from  we get tce, from , we concIn order to complete the proof of Theorem ent, cf. .(i)If there exists no asymptotic cone of (ii)Assume that there exists Let In the proof for simplicity we denote X, the X.Let us first prove item (i). Let us assume by contradiction that there exist, for every p and C splits a line. But this is not possible, since C is an asymptotic cone of Y that by hypothesis does not split a line, thus giving the sought contradiction.Hence, let us take ce, from  we also mentclasspt{minimaLet us now prove item (ii). Since i, Since we have that C with tip X and (It is a direct consequence of Lemma As a consequence of item (ii) of Lemma esult in .M and every V on Let We are in the setting of item (ii) of Lemma Now if N are isometric to More generally, if We record a straightforward consequence of the above statement.Let smooth outside the tip, i.e., it is of the form C(Z) where Z is a smooth closed manifold. Then for every volume V on Assume that every asymptotic cone of M is smooth outside the tip, then =2t. By  we have\C splits a Euclidean factor, there exist So we are left to prove . Given \Toponogov’s theorem implies that her with , give\doThe statement of Theorem The proof immediately follows from Theorem


Individual responses to methylphenidate (MPH) can significantly differ in children with attention-deficit/hyperactivity disorder (ADHD) in terms of the extent of clinical amelioration, optimal dosage needed, possible side effects, and short- and long-term duration of the benefits. In the present repeated-measures observational study, we undertook a proof-of-concept study to determine whether clustering analysis could be useful to characterize different clusters of responses to MPH in children with ADHD. We recruited 33 children with ADHD who underwent a comprehensive clinical, cognitive, and neurophysiological assessment before and after one month of MPH treatment. Symptomatology changes were assessed by parents and clinicians. The neuropsychological measures used comprised pen-and-paper and computerized tasks. Functional near-infrared spectroscopy was used to measure cortical hemodynamic activation during an attentional task. We developed an unsupervised machine learning algorithm to characterize the possible clusters of responses to MPH in our multimodal data. A symptomatology improvement was observed for both clinical and neuropsychological measures. Our model identified distinct clusters of amelioration that were related to symptom severity and visual-attentional performances. The present findings provide preliminary evidence that clustering analysis can potentially be useful in identifying different responses to MPH in children with ADHD, highlighting the importance of a personalized medicine approach within the clinical framework. Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that is characterized by a persistent behavioral and cognitive pattern that includes inattention, motor hyperactivity, and impulsivity symptoms that manifest inconsistently with age or developmental level ,2. ADHD Treatment strategies for ADHD include the combination of cognitive-behavioral therapy and pharmacological treatment; the latter is recommended for children aged 6–11 years and preferred for children aged 12 years and older ,8. MPH rAround 70% of children with ADHD show a clinically significant positive response to MPH medication ,12. HundAccordingly, over the last two decades, the psychopharmacological research field has seen rapid growth in functional near-infrared spectroscopy (fNIRS) as a tool to monitor functional brain activity in children with neurodevelopmental conditions and to explore possible relationships between pharmacological improvement and neurophysiological activation. fNIRS has several advantages over other neuroimaging modalities, especially for children with ADHD, since it is harmless, tolerant to body movements, and portable . InteresThe above-mentioned research evidence suggests that, currently, there is an urgent need to address individual characteristics that are associated with the response to MPH. Personalized medicine can represent a useful approach to meet the needs of each patient, to identify specific treatment plans that are based on possible clusters of response to intervention, and, in conclusion, to reduce the long-term costs of mental health . Within Within the personalized medicine field, recent studies have applied machine learning (ML) algorithms to predict treatment outcomes (in the supervised framework) or to detect clinical sample characteristics (in the unsupervised area). ML is an ensemble of traditional statistical procedures and computer algorithms that are increasingly used as a useful tool for identifying treatment response characteristics in health care applications ,28. SpecThrough a bivariate model-based clustering analysis, Reimherr and colleagues  found noDespite some studies using an ML approach, the unsupervised, bottom-up framework in psychiatry is still barely explored; indeed, an ML model that addresses the specificities that are detected in response to MPH is not currently available. Further progress in this field could provide support for effective clinical decisions through a personalized treatment approach.Given those premises, the aim of our study was to evaluate the existence of clusters of clinical, neuropsychological, and neurophysiological responses to MPH treatment in children with ADHD after a period of standardized pharmacological intervention. Particularly, we aimed to evaluate the response to MPH therapy for ADHD children through a bottom-up approach, without an a priori selection of treatment outcome measures. To achieve this aim, we implemented an unsupervised algorithm (finite Gaussian mixture model (FGMM)) for clustering . We expeThe present work is a repeated-measures observational study representing the extension of a cross-sectional observational work that was previously published by our research group . We recrThe study design is depicted in A sample of thirty-three drug-naïve children with ADHD between the ages of 6 and 16 years (mean age = 11 ± 3.2 years) was recruited. For all patients, the ADHD diagnosis was made according to the DSM-5 criteria . Thirty-The control group was composed of twenty-seven TD children that were recruited by local pediatricians and from schools in the same areas of ADHD children to be age- and gender-matched with the clinical group. The presence of psychiatric disorders was excluded using the DAWBA parent diagnostic interview.The exclusion criteria included the presence of intellectual disability, neurological diseases, epilepsy, genetic syndromes, and previous treatment with psychoactive drugs. A diagnosis of other psychiatric disorders ) was not an exclusion criterion: 50% of the participants also had a diagnosis of an SLD, 17% of the participants also had an oppositional defiant disorder, 13% of the participants also suffered from an anxiety disorder, 8% of the participants also had autism spectrum disorder, and 13% of the participants also had a mood disorder. A total of 21% of the patient’s sample received ADHD as a single diagnosis. All participants were Caucasian, spoke fluent Italian, and had normal or corrected-to-normal vision. Familial socioeconomic status (SES) was coded according to the Hollingshead scale for parental employment .(1)Full-Scale Intelligence Quotient (FSIQ)The FSIQ was calculated using a short form of the Wechsler Intelligence Scale for Children-III or -IV (WISC-III or -IV) ,41,42 fo(2)Clinical and behavioral measuresConners’ Parent Rating Scales-Revised (CPRS-R): Clinical and behavioral profiles were assessed through the Italian version of the CPRS-R ,44, whicClinical Global Impression-Severity (CGI-S): Clinical severity and global functioning were also assessed by the clinician through the CGI-S , a one-iChildren-Global Assessment Scale (C-GAS): The clinician also compiled the C-GAS , which i(3)Neuropsychological measuresAmsterdam Neuropsychological Tasks (ANT): ANT  is a comNEPSY-Second Edition: NEPSY-II  is a mul(4)Stimulation protocolThe Emotional Continuous Performance Task (e-CPT) ,50 was p(5)fNIRS data acquisitionDetails regarding the fNIRS data acquisition are described in Mauri and colleagues’ work . Optode Statistical analyses were performed using R version 4.1.0 statistical software  with thePrimarily, a between-group analysis was performed to characterize the group of children with ADHD compared to the TD group. More information about the between-group analysis can be found in the According to the variable distribution, a Wilcoxon signed-rank test was used as a repeated-measures analysis for evaluating the changes in clinical , neuropsychological (ANT and NEPSY), and NIRS variables within the group of children with ADHD after one month of MPH treatment.Afterward, an FGMM was applied to the clinical and neuropsychological variables in which the patients showed the highest significant improvement after the MPH treatment; only subjects that were evaluated at both waves were included in the analyses. FGMMs were built through an unsupervised learning algorithm, which selected the optimal number of components from multivariate distributions . The meaTwo participants with ADHD and two participants in the TD group were later excluded based on recruitment criteria, and four children with ADHD were excluded due to noncompliance with behavioral tasks, as mentioned in our previous work . Twenty-Between-group analyses were performed to characterize our sample and to identify the differences and similarities from the TD group. The between-group analyses showed no significant differences in FSIQ and SES between the ADHD group and TD, but significant differences in several clinical, neuropsychological, and neurophysiological variables. For a summary of the clinical, neuropsychological, and neurophysiological characteristics of the two samples, see p < 0.05) were found in clinical and neuropsychological domains  in bivariate FGMMs, separately for each subscale. The NEPSY and ANT scales in which the patients showed a significant improvement were considered jointly (T0 and T1) in bivariate FGMMs, separately for each subscale. The BIC values of the ANT–FA4L reaction time scale model suggested the presence of two clusters. Those clusters did not differ in terms of the distribution of psychiatric comorbidities within each cluster. The clusters’ estimated results are summarized in The evaluation of the response to MPH treatment in ADHD is currently performed through a top-down approach, with an a priori selection of an outcome measure and an associated analysis that addresses the predictive ability of one or more other measures . The maiAt T0, we also collected data from an age- and gender-matched sample of 25 typically developing (TD) children in order to characterize our clinical sample compared to a control group. As expected, children with ADHD presented higher psychopathological traits and worse neuropsychological performances. Moreover, differences at the neurophysiological level were found .Regarding the response to MPH, children with ADHD exhibited a significant improvement in clinical symptomatology after one month of MPH administration. These results were expected and in line with the literature, highlighting the well-documented effects of MPH administration ,59,60. ILastly, no significant change was found regarding the brain hemodynamic activation after the MPH treatment. This result was not in line with the recent review of Grazioli and colleagues  in whichNext, in order to find possible patterns of behavioral and attentive responses to MPH, FGMM clustering analyses were implemented using clinical and neuropsychological measures that showed a significant improvement. To our knowledge, this is the first study to use an unsupervised algorithm to identify clusters of symptom improvements in children with ADHD.Our analysis suggested the existence of two clusters of subjects in several psychopathology areas, as described by the parents using the CPRS-R. This finding confirmed the high variability in the clinical response of children with ADHD ,17. On tWith specific respect to the CPRS ADHD index subscale, the results highlighted that the majority of children showed a significant improvement after the MPH treatment, with a score under the clinical cut-off, whereas a small portion of children had lesser improvements. This result could represent corroboration of previous research results addressing specificities of MPH efficacy ,12 throuConcerning the CPRS cognitive problems subscale, the largest cluster of patients showed mean before MPH values within the clinical band and, after MPH, within the nonclinical range; the smallest cluster was represented by subjects showing both before- and after-MPH mean values within the clinical range. In this case, we observed a large improvement in both groups of subjects despite the smallest cluster showing more severe before-treatment mean scores. This result is in line with what was reported in the literature addressing improvements in the attentional domain after MPH treatment ,12.Regarding the perfectionism subscales, we found the biggest group showing both before- and after-MPH mean values below the clinical range and a small improvement; the smallest group showed mean values within the clinical band before the MPH treatment and a large improvement after the MPH treatment.Lastly, FGMM clustering analyses were implemented for neuropsychological variables. Two clusters of subjects were detected regarding the ANT–FA4L RT. Specifically, the largest group showed a high reaction time before the MPH treatment and a remarkable improvement after the MPH treatment, as suggested by previous studies on visual attention capacity ; on the The between-clusters differences identified by our analysis did not appear to be due to the psychiatric comorbidities of children with ADHD, with the exception of differences in CPRS cognitive problems subscale. In that case, children with ADHD and comorbid SLD were more frequent (80% vs. 20%) in the cluster of children with scores in the clinical range before and after the MPH treatment. While this is not surprising because the CPRS cognitive problems subscale specifically identifies children having difficulties at school, this preliminary result suggested that SLD could negatively interfere with the treatment for the inattention domain, as previously described in recent literature .The novelty of our study is represented by the use of an unsupervised ML algorithm to identify hidden clusters of patients that are characterized by specific clinical and neuropsychological modifications following pharmacotherapy. Notably, children with ADHD do not seem to constitute a single homogeneous group; on the contrary, it is likely that a single clinical diagnosis could “mask” the existence of various phenotypes of response to MPH, as evidenced by our results in terms of clinical improvement and neuropsychological performances. This is in line with the current literature that reports MPH’s efficacy to be around 70%, highlighting that approximately 30% of children do not positively respond to MPH treatment ,67. HencThe main research and clinical implication that was derived from the present work is the use of a bottom-up approach in the study of responses to treatment. Indeed, the adoption of an unsupervised ML analysis rather than an a priori choice of outcome measures could offer new insights into detecting specificities in response to MPH. Indeed, clustering analysis does not require any theoretical assumption, thus offering clinical and research evidence that is useful for a personalized medicine approach.Despite the mentioned preliminary results, some limitations must be addressed. First, our ML analysis could be highly specific to our clinical group, in consideration of the small sample size. Future investigations could employ a bigger sample size and verify the validity and direction of the present preliminary findings. Furthermore, our small sample did not allow us to fully investigate the role of psychopathological comorbidities in the different responses to medical treatment. In addition, the present study was not designed to include a second evaluation for the control group, which would have provided a possible comparison with children with ADHD after the MPH administration. Future clustering analyses could explore the differences between children with ADHD and children with other psychiatric conditions and TD groups. Lastly, our repeated-measures work had a brief duration (one month of MPH administration), which did not allow us to evaluate the maintenance of these effects over a long period.In conclusion, our findings confirmed that MPH treatment was followed by clinical and neuropsychological improvements in children with ADHD, as highlighted by previous research. The novelty of our study consisted in the implementation of a rarely used analysis method, which demonstrated that clinical and neuropsychological ameliorations could be inhomogeneous among all patients and clearly indicated the existence of clusters of subjects that improved in different ways. The present ML method, if replicated in future studies with larger samples, could provide the first step toward identifying specific clusters of MPH responses. An ML approach, together with traditional statistical tools, may be promising in detecting characteristics of treatment response, and it could represent the key to exploring personalized medical necessities.


Bartonella species, which include B. henselae, B. koehlerae, and B. clarridgeiae, can cause mild to severe illness in humans. In the present study, we evaluated 1362 serum samples obtained from domestic cats across the U.S. for seroreactivity against three species and two strain types of Bartonella associated with cats  using an indirect immunofluorescent assay (IFA). Overall, the seroprevalence at the cutoff titer level of ≥1:64 was 23.1%. Seroreactivity was 11.1% and 3.7% at the titer level cutoff of ≥1:128 and at the cutoff of ≥1:256, respectively. The highest observation of seroreactivity occurred in the East South-Central, South Atlantic, West North-Central, and West South-Central regions. The lowest seroreactivity was detected in the East North-Central, Middle Atlantic, Mountain, New England, and Pacific regions. We observed reactivity against all four Bartonella spp. antigens in samples from eight out of the nine U.S. geographic regions.Cat-associated  Bartonella species, which include B. henselae, B. clarridgeiae, and B. koehlerae [Ctenocephalides felis) serves as the main vector [Bartonella infected cats are typically asymptomatic; however, there is some evidence that B. henselae is associated with febrile illness, endocarditis, myocarditis, and ocular disease [B. henselae [Domestic and wild felines are the natural vertebrate reservoirs for several zoonotic oehlerae ,2. The cn vector ,3. Thesen vector ,5. Barto disease ,7,8. Furhenselae .Bartonella species infect humans exposed to an infected animal or its ectoparasites, typically through a scratch contaminated with infected flea feces [B. henselae infection in humans are collectively known as Cat Scratch Disease (CSD). Symptomatic individuals experience fever, headache, and regional lymphadenopathy [Bartonella clarridgeiae can cause a typical CSD presentation, including symptoms such as inoculation papules, fever, and lymphadenopathy [B. koehlerae can cause a variety of symptoms, including fever, fatigue, muscle and joint pain, neurological complications, blurred vision, hallucinations, and endocarditis [Bartonella research progresses, more information is known about mammalian hosts, ectoparasite vectors, transmission, and clinical symptoms. As the designation of CSD is limited to only two cat-associated Bartonella species, exposure to cats or their fleas, and defined symptoms, the term “bartonellosis” is more appropriate as it encompasses a broad range of disease presentation, routes of transmission, and Bartonella species.These cat-associated ea feces . The clienopathy ,11. Moreenopathy ,17,18,19enopathy . While ncarditis ,22,23,24In 2016, Nelson et al.  analyzedBartonella species can provide important information for predicting the risk of acquiring bartonellosis in an area. In the U.S., the only large-scale regional Bartonella serosurvey found around 28% of cats tested positive [Bartonella spp. seroprevalence ranging from 6% to 67% [Serosurveys of cat populations for the presence and infection prevalence of positive . Regionapositive . Since t% to 67% ,26,27.Bartonella exposure of domestic cats; however, they investigated only two of the three known cat-associated Bartonella species, B. henselae and B. clarridgeiae. Additionally, Jameson et al. [B. henselae, which was the only variant known at that time. Currently, two predominant types of B. henselae, differentiated by genetic and antigenic characteristics, are associated with human illness: B. henselae type 1 (type strain Houston-1) and B. henselae type 2 (type strain Marseilles) [B. henselae type 1 and B. henselae type 2. Guptill et al. [B. henselae isolates collected from cats in California were type 2, while in Florida, half of the cat isolates were B. henselae type 1 and the other half were B. henselae type 2. Little information is available on the exposure of domestic cats in the U.S. to B. koehlerae, as compared to other cat-associated Bartonella species.These studies have provided valuable information on the n et al.  estimateseilles) ,27,28,29seilles) . Limitedl et al.  reportedBartonella species and to assess the frequency of co-exposures or multi-reactivity among the selected Bartonella IFA antigens. Our objectives were to evaluate prevalence in domestic cats from different parts of the U.S. of Bartonella antibodies to three species and two strains of Bartonella antigen  using an indirect immunofluorescence assay (IFA).The purpose of our study was to update the information on the regional exposure of U.S. domestic cats to cat-associated Bartonella antigens at the cutoff titer of ≥1:64. This number decreased to 11.1%  and 3.7%  when the antibody cutoff titers of ≥1:128 and ≥1:256 were considered reactive, respectively. The regional seroprevalence at the cutoff titers ≥1:64, ≥1:128, and ≥1:256 for all antigens was higher in East South-Central, South Atlantic, West South-Central, and West North-Central regions and lowest in East North-Central, Middle Atlantic, Mountain, New England, and Pacific regions  of cat serum samples were seroreactive against at least one of the four cat-associated  regions .B. henselae antigens (type 1 or type 2), 36.9%  were reactive to the B. koehlerae antigen, and 8.9%  were reactive to the B. clarridgeiae antigen. The observed seroreactive samples for each of the four antigens tested can be seen in When evaluating only the reactive sera at the cutoff titer value of ≥1:64, approximately 31.2%  were reactive to at least one of the Sera that were reactive to more than one antigen were classified as multi-reactive. These multi-reactive sera accounted for 6.5% , 2.6% , and 0.4%  of sera at the cutoff titers of ≥1:64, ≥1:128, and ≥1:256, respectively. Only cats from the Pacific (1 of 414) and South Atlantic (5 of 403) regions had multi-reactive samples at a cutoff titer of ≥1:256, and below this titer multi-reactive sera were observed in all regions .B. clarridgeiae when seroreactive to both B. henselae type 1 and B. henselae type 2 . Considering seroreactivity at titers ≥1:128, there was a slightly lower but similar prevalence ratio of 0.06 (95% CI: 0.03–0.12) of being seroreactive to B. koehlerae when already seroreactive to both B. henselae type 1 and B. henselae type 2 as well as a prevalence ratio of 0.56 (95% CI: 0.0.36–0.88) of being seroreactive to B. clarridgeiae when already seroreactive to both B. henselae type 1 and B. koehlerae. The Poisson regression model was also used to estimate that samples that were B. henselae type 1 seroreactive had a prevalence ratio of 0.22 (95% CI: 0.14–0.33) to being reactive to B. henselae type 2. Similarly, samples had a prevalence ratio of 0.15 (95% CI: 0.09–0.25) of being seroreactive to B. koehlerae infection when already reactive to B. henselae type 2  of being seroreactive to e type 2 .B. koehlerae was found in all regions and B. koehlerae was the most reactive antigen, with a total prevalence of 8.5% , 3.7% , and 1.3%  at the cutoff titer values of ≥1:64, ≥1:128, and ≥1:256, respectively  samples for which the age of the cat was available and titer could be evaluated. Roughly 3.3% (95% CI: 2.0–5.5%) of kittens/young cats (0–2.9 y), 3.7% (95% CI: 2.4–5.5%) of adult cats (3–10.9 y), and 4.0% (95% CI: 2.3–7.1%) of seniors (11 + y) were reactive for at least one antigen at a titer of ≥1:256. Adult cats accounted for over 40% of the reactive samples at titers of ≥1:64, ≥1:128, and ≥1:256. The titer results by cat age can be seen in B. henselae type 1, B. clarridgeiae was less likely to result in positive titers, with a RR = 0.75 (95% CI: 0.0.64–0.88), B. koehlerae was more likely to result in positive titers, with RR = 1.44 (95% CI: 1.27–1.64), and the B. henselae type 2 rate of resulting in positive titers was no different, with RR = 0.92 (95% CI: 0.79–1.08). Age group and region jointly contributed to the probability of a seroreactive titer . For the count portion of the model, no predictors statistically significantly contributed, but if a sample crossed the seroreactive threshold, the mean titer was 55.4 (95% CI: 53.3–57.5). From the hurdle model, the type of antigen, age, and region all contributed as predictors to the binomial portion, which modeled the rate ratio (RR) of a sample having positive titer. Compared to Bartonella spp. antigens of 23.1% at a cutoff titer of ≥1:64 was slightly lower than the 28% overall seroprevalence in U.S. cats reported by Jameson et al. [B. henselae type 1. In our study, if we only evaluate B. henselae type 1 reactive and multi-reactive samples at a titer of ≥1:64, the seroprevalence is 7.5% .In this study, the overall seroprevalence against the n et al. . That stB. koehlerae was the most reactive antigen at all titer levels, and seroreactivity to it was observed in all regions. The two B. henselae antigens were the next most reactive antigens, followed by B. clarridgeiae. In dogs, Lashnits et al. [B. koehlerae seroreactivity from all U.S. regions and a similar seroprevalence between B. henselae (2.13%) and B. koehlerae (2.39%) [B. henselae antibodies have been detected in cats [B. koehlerae antigen has been included in few feline serological investigations. In Spain, 41.6% of veterinary personnel were seroreactive against the B. koehlerae antigen, while 37% were reactive against B. henselae, suggesting B. koehlerae is prevalent in Spanish cat or dog populations [B. clarridgeiae and B. koehlerae to be more prevalent than B. henselae in stray cats, using molecular techniques [B. koehlerae antigen in order to better understand seroprevalence in cat populations.In our study, s et al.  reported 2.39%) . Around 9% . Arou in cats . Howeverulations . A studychniques . Future C. felis is the main vector for transmission of these Bartonella species among cats and prefers a humid environment [Bartonella spp. antigens  [Bartonella spp. antigens [Bartonella antigens tested [Overall, the observed regional variation in seroprevalence, with a higher prevalence at the ≥1:64 titers in the southern regions  and the West North-Central region (41.5%) than in western regions , the East North-Central region (20.0%), and northeastern regions . These results are similar to those of Jameson et al. [ironment ,34. Simiironment , a greatkhoffii) . In thatantigens . The auts tested . FurtherBartonella serological assay, interpreting titer results can be complicated [Bartonella species, or if their serum is just cross-reactive. A cat can be exposed to multiple Bartonella species in its lifetime, and Bartonella co-infected cats have been documented [B. henselae and Bartonella quintana antigens may cross-react when testing human sera [Bartonella species.Although IFA is a common plicated ,30,36. Splicated ,37. Someplicated . Using ccumented . In addiman sera , so it iB. henselae type 1 was less likely to also be multi-reactive for B. henselae type 2, and if a sample was reactive for B. henselae type 2 then it was less likely to also be multi-reactive for B. koehlerae. A study analyzing the specificity of Bartonella antigens in dogs found that experimentally infected animals only reacted to the specific Bartonella species they had been infected with and did not cross-react with other Bartonella antigens [Bartonella species rather than cross-reactivity [Bartonella spp. antigens was less than 3% in 15,451 diagnostic sera submitted by veterinarians suspecting infection with a vector-borne pathogen further supports IFA specificity when testing dogs [Bartonella IFA sensitivity is poor when testing dogs with PCR-confirmed infections [Bartonella species, cross-reactivity, or differences in criteria for the endpoint titer evaluation.We observed multi-reactive cats ranging from 0.4% at titers ≥1:256 to 6.5% at titers ≥1:64 and found some associations between antigens of multi-reactive samples. For example, a sample reactive for antigens . The autactivity . Furthering dogs . Regrettfections ,42. ThusBartonella infections in cats [Bartonella spp. as a cause of human illnesses, there should be increased public health and veterinary emphasis on flea control measures.Several limitations exist in our study. The sample set is not fully representative of the U.S. cat population due to unknown criteria for the selection, cat health data, travel history, and discrepancies in availability of the samples by a region. Specifically, selection bias may exist towards cats that are well cared for because an owner would have to be willing to pay for testing services. Additionally, if the animals are well cared for, they may also be using flea control products. Flea control practices for house cats have likely become more popular since the early 1990s due to increased client education and the development of new flea control products. Since 1997, several new active ingredients and combination ingredients have been registered for flea control and are available for convenient use as oral and topical treatments for companion animals . The use in cats ,44,45. CBartonella diagnostic techniques include IFA testing on acute and convalescent sera, using B. henselae and B. quintana antigens. Dalton et al. [Bartonella antigens at an overall seroprevalence at the ≥1:64 titer of 23.1%. Although the overall seroprevalence is low, we observed exposure to B. clarridgeiae and B. koehlerae, but not to B. henselae, in half of the seroreactive cats across all titer levels examined. Consequently, people may be exposed to Bartonella species that may not be detected by traditional B. henselae and B. quintana IFA assays.Currently, human n et al. , when evBartonella species; however, more research is needed to investigate this observation and potential association. Possible sample biases in this study have been identified, and future efforts should employ formal, statistical sampling methods to ameliorate these concerns. Furthermore, efforts to increase sample number across regions and to consider the animal’s health when characterizing historical Bartonella species exposures would strengthen conclusions. Nevertheless, from this observational study, we observed similarities between cat-associated Bartonella species seroreactivity and the overall regional proportion of human CSD cases. We were also able to document cat seroreactivity in the U.S. to B. koehlerae, a Bartonella spp. that has been associated with human illness but not classical CSD.Our results demonstrate differences in seroreactivity of cats to We tested archived cat serum samples, originally submitted for suspected illness or blood donor screenings to three veterinary diagnostic laboratories, located at Colorado State University, the University of California at Davis, and North Carolina State University between 2008 and 2017. In total, 1362 cat serum samples from 38 states and Washington, D.C. were analyzed. The 38 states and Washington, D.C. were grouped to represent the nine geographic regions of the continental U.S., following the scheme used by Nelson et al.  Table 5Table 5. Bartonella species of interest . Additionally, the slides were prepared using 48-well microscope slides  and stored at −20 °C for future use. The Vero E6 cell line used to produce the antigen was obtained from the Centers for Disease Control and Prevention, Ft. Collins, Colorado. Positive and negative controls were included with each run. The positive controls against each of the four cat-associated Bartonella antigens were custom mouse antibodies created by ProSci Incorporated . We used anti-cat FITC-labeled IgG (H + L) conjugate  for samples and anti-mouse FITC-labeled IgG (H + L) conjugate  for the controls.The cat sera were tested by an IFA. The antigen production, slide preparation, and IFA procedure were performed with slight modifications, according to a previously published protocol . The modEach well was scored for fluorescent reactivity, and any reactive sample at the screening dilution of 1:32 was further diluted twofold until the sample was no longer reactive. The last dilution at which florescence was observed for a specific antigen was recorded as the final cutoff titer. Seroreactive results were evaluated separately by two investigators. For statistical analyses, reactive antibody cutoff titers of ≥1:64, ≥1:128, and ≥1:256 were each evaluated.To evaluate associations of cat age, region of collection, and antigen type with antibody titers, data were analyzed using a hurdle regression model. The hurdle model has two components, one to model the probability of a sample having a positive titer using logistic regression with a log link, and the other to model the value of the titer among those for which it is positive using a zero-truncated Poisson regression model. Bootstrapping was used to compute 95% confidence intervals (CIs) for model parameters. A Poisson regression model was used to assess cross-reactivity of the four antigens at each of the three cutoff levels. Prevalence ratios (PRs) and 95% CIs were calculated to compare antigen reactivity. Statistical analyses were conducted using the “countreg”, “multcomp”, and “boot” packages in R version 3.6.1 .