Transdiagnostic monoamine-based subtyping for attention- deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning

Transdiagnostic monoamine-based subtyping for attention- deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transdiagnostic monoamine-based subtyping for attention- deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning Masatoshi Yamashita, Qiulu Shou, Sayo Hamatani, Hideo Matsuzaki, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8477091/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Neuroimaging and molecular studies have attempted to elucidate the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, their findings have been inconsistent because of within-disorder heterogeneity and cross-disorder phenotypic overlap. We sought to identify monoamine-based subtypes across ADHD and ASD and to clarify their distinct brain structural characteristics. In 83 children with ADHD and/or ASD, we applied unsupervised machine learning (NbClust with K-means) to identify novel neurodevelopmental disorder (NDD) phenotypes using urinary monoamine metabolite profiles. Behavioral symptoms, cognitive performance, cortical surface area, and gray matter volume (GMV) were then evaluated for each NDD phenotype and for 83 children with typical development (TD). Clustering identified two NDD phenotypes: NDD-A (n = 18, characterized by high levels of 4-hydroxy-3-methoxyphenylglycol, 5-hydroxyindoleacetic acid, and homovanillic acid) and NDD-B (n = 65, characterized by low levels of these monoamine metabolites). Moreover, urinary 4-hydroxy-3-methoxyphenylglycol levels correlated positively with social communication difficulties in NDD-A. Behaviorally, the NDD-B group showed significantly lower levels of cognitive control, cognitive flexibility, and inhibitory control than did the TD group. Structurally, compared to the TD group, the NDD-A group showed significant surface area enlargement in the isthmus cingulate gyrus, whereas the NDD-B group exhibited significant GMV reductions in the frontal lobe, precuneus, posterior cingulate cortex, and superior occipital area. These findings suggest that monoaminergic hyper- or hypofunction in individuals with NDD-A and NDD-B is associated with distinct brain structural differences. Such phenotype specificity may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps. brain structure cross-disorder phenotypic overlap monoamine metabolite neurodevelopmental disorder unsupervised machine learning Figures Figure 1 Figure 2 Figure 3 Introduction Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are common neurodevelopmental disorders (NDDs) in children and adolescents, with prevalence rates of > 5% and > 1.5% (Baio et al. 2018 ; Thomas et al. 2015 ), respectively. ADHD symptoms include inattention, hyperactivity, and impulsivity, whereas ASD is characterized by difficulties in social communication and interaction, restricted and repetitive behavioral patterns, and atypical sensory responses (Lane et al. 2010 ). Although the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides diagnostic criteria for these conditions, the symptoms of children with ADHD and ASD may not fit within the boundaries of a single disorder, as considerable clinical overlap (Grzadzinski et al. 2016 ), a high comorbidity rate (Simonoff et al. 2008 ), and within-disorder heterogeneity (Yamashita et al. 2024 ) have been observed. Additionally, ADHD and ASD are associated with executive function differences (Townes et al. 2023 ), increased likelihood of various psychiatric disorders, fatigue, and sleep disorder (Antshel et al. 2016 ; Axelsson et al. 2024 ), highlighting the neurobiological overlap. As ADHD and ASD may be viewed as different manifestations of the same overarching disorder (Antshel and Russo 2019 ), establishing NDD phenotypes based on biomarkers, such as magnetic resonance imaging (MRI) parameters and biochemical indices, is important for biologically informed classifications. Previous MRI studies have reported that, compared to typically developing (TD) individuals, individuals with ADHD show decreased gray matter volumes (GMVs) in the frontal regions, precuneus, and basal ganglia (Moreno-Alcázar et al. 2016 ; Vilgis et al. 2016 ; Zhao et al. 2020 ). GMV reductions in the frontal-temporal regions, precuneus, amygdala, and cerebellum have also been reported in individuals with ASD compared to TD individuals (Li et al. 2019 ; Sato et al. 2017 ; Yang et al. 2018a ). Nonetheless, such reductions have not been reported in other studies (Bonilha et al. 2008 ; Seidman et al. 2011 ; Semrud-Clikeman et al. 2014 ). These inconsistent results may reflect the phenotypic heterogeneity of NDDs. Additionally, several studies have reported that ADHD and ASD share overlapping brain structural characteristics in regions such as the medial temporal lobe, inferior parietal cortex, and cerebellum (Brieber et al. 2007 ; Dougherty et al. 2016 ). However, these studies on brain structure have also not always yielded consistent results. Hence, while clinical guidelines exist for ADHD pharmacotherapy, clear biomarker-based criteria to guide individualized medication selection, particularly for individuals with co-occurring ADHD and ASD, remain limited, and definitive biomarkers for diagnosis have yet to be established. Monoamines represent a candidate biomarker, given its association with the pharmacological targets of therapeutic drugs for ADHD and ASD, such as methylphenidate, atomoxetine, and risperidone (Hellings 2023 ; Mechler et al. 2022 ; Ota et al. 2021 ). Previous neuroimaging and pharmacological studies have reported that individuals with ADHD show decreased dopamine and noradrenaline and increased serotonin levels compared to control groups (Hannestad et al. 2010 ; Nikolaus et al. 2022 ; Parlatini et al. 2024 ). Furthermore, reduced dopamine and increased serotonin levels have been reported in individuals with ASD compared to control groups (Brandenburg et al. 2020 ; Chugani et al. 1999 ; Goldberg et al. 2009 ). Although these findings suggest that complex changes between monoamines are involved in the molecular mechanisms underlying these NDDs, the evidence has been inconsistent (Alabdali et al. 2014 ; Karlsson et al. 2013 ; Schalbroeck et al. 2021 ; Ulke et al. 2019 ; Wiers et al. 2018 ). This may be due to the diversity within these disorders as well as their neurobiological overlap, as ADHD and ASD are not single conditions, but rather represent an aggregate of heterogeneous mechanisms. In this context, investigating peripheral monoaminergic indices may help capture biologically relevant inter-individual variability. Additionally, ADHD and ASD have been associated with alterations in the peripheral monoaminergic system, as indicated by changes in the urinary excretion of monoamines and their metabolites, such as 4-hydroxy-3-methoxyphenylglycol (MHPG, a noradrenaline metabolite), 5-hydroxyindoleacetic acid (5-HIAA, a serotonin metabolite), and homovanillic acid (HVA, a dopamine metabolite) (Dvoráková et al. 2007 ; Gevi et al. 2020 ; Gevi et al. 2016 ). As urinary monoamine metabolites (MMs) are transported from the brain via the organic anion transporter 3 system in the blood–brain barrier (Mori et al. 2003 ; Ohtsuki et al. 2003 ), they may serve as valuable noninvasive biomarkers of monoamine activity in the brain (Amin et al. 1992 ; Yamashita and Yamamoto 2021 ). Elucidating the brain structural characteristics associated with urinary MM subtypes of NDD may help to address the inconsistent findings associated with within-disorder heterogeneity and cross-disorder phenotypic overlap. Nevertheless, investigations of the brain structure of NDD subtypes to date have been limited to clinical symptom- and executive function-related subtypes (Cordova et al. 2020 ; Zhang et al. 2022 ), and little is known about NDD subtypes classified by urinary MM patterns. We theorized that a better understanding of within-disorder heterogeneity and cross-disorder overlap may be helpful when considering prognosis or informing treatment planning. Thus, this study aimed to clarify this gap in knowledge by investigating whether transdiagnostic ADHD and ASD subtypes, defined by urinary MM profiles, would show differences in brain structure. We hypothesized that, when using an unsupervised machine learning approach, children with these NDDs would be categorized into at least two clusters based on urinary levels of MHPG, 5-HIAA, and HVA. We further hypothesized that these monoamine-based subtypes would show distinguishable brain structural profiles (e.g., regional GMV and cortical surface area) as compared to TD children, and that these structural profiles would differ between NDD subtypes. We also examined cognitive performance to support the interpretation and characterization of the identified subtypes. Methods Participants The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061) and the study procedures complied with the principles of the Declaration of Helsinki. All parents provided written informed consent, and all children provided assent for participation. The study involved 172 children (TD group: n = 86; NDD group: n = 86; age: 6−17 years). Children who were diagnosed with ADHD, ASD, or co-occurring diagnoses based on the DSM-5 were recruited through a hospital at the University of Fukui, Japan. TD children who did not receive special support education were recruited from the local community. All participants had a Wechsler full-scale intelligence quotient score ≥ 70 ( Table 1 ); no history of neurological, cardiovascular, or psychiatric illness; no contraindication for MRI; and had discontinued medication based on its half-life prior to the experiment. Data from three TD children were excluded from the analysis because of claustrophobia during the MRI scan. Furthermore, data from three children with NDDs were excluded because of physical deconditioning and claustrophobia during the MRI scan. Thus, data from 83 TD children and 83 children with NDDs were used in the final analysis ( Table 1 ). Psychological and cognitive measurements The psychological questionnaire targeted the Japanese version of the Social Responsiveness Scale (SRS-2), Short Sensory Profile, Conners 3 Scale, Chalder Fatigue Scale, and Munich Chronotype Questionnaire. Furthermore, cognitive tests included the stop signal and spatial working memory tasks from the Cambridge Neuropsychological Tasks Automated Battery and parts A and B of the Trail-Making Test (TMT). For the details of each questionnaire and cognitive function task, see Online Resource 1 . Determination of MMs Prior to testing, the children were instructed to refrain from intense physical activity and certain foods and beverages, such as alcohol, coffee, high-fat fish, red meat, and blue cheese, which can be difficult to digest, for 24 h (Yamashita and Yamamoto 2021). On the test day, fresh urine was collected after the children had been allowed to rest for 30 min. Urine samples were diluted with 6.7 mM hydrochloric acid and 2.5% perchloric acid to separate albumin, as previously reported (Yamashita and Yamamoto 2021) . Obtained supernatants were stored at -80 °C until high-performance liquid chromatography (HPLC) was performed. Urinary MHPG, 5-HIAA, and HVA (Sigma-Aldrich Inc., St Louis, MO, USA) content was measured using an HPLC device with an electrochemical detector (Nanospace SI-2 3005, Osaka Soda Corporation, Osaka, Japan) and a chromate recorder (C-R8A; Shimadzu Corporation, Kyoto, Japan). The mobile phase consisted of 15% methanol in a solution (pH 4.13) containing 30 mM citric acid, 10 mM disodium hydrogen phosphate, 0.5 mM sodium octyl sulfate, 50 mM sodium chloride, and 0.05 mM ethylenediaminetetraacetic acid, as previously reported (Yamashita and Yamamoto 2017; 2021) . This was pumped through a 5-μM C 18 column (150 mm × 4.6 mm; TSK gel, ODS-80TM, Tosoh, Tokyo, Japan) at a flow rate of 0.7 mL/min on a single pump (Nanospace SI-2 3101; Osaka Soda Corp., Osaka, Japan) and temperature of 25°C in the column oven (Nanospace SI-I 2004; Shiseido Corporation, Tokyo, Japan). MHPG, 5-HIAA, and HVA levels were detected electrochemically (voltage: 700 mV) with retention times of approximately 6, 12, and 18 min, respectively. Statistical analysis of demographic, psychological, cognitive, and MMs data Statistical analyses were conducted using R (version 4.3.0; The R Foundation for Statistical Computing, Vienna, Austria), except for voxel-based morphometry (VBM) statistical analysis. All data were initially assessed for normality and homogeneity of variance, and statistical tests were determined accordingly. Differences between groups based on demographic and behavioral symptoms data were analyzed using Student’s t -test, Welch’s t -test, Chi-square test, one-way analysis of variance (ANOVA), and Welch’s one-way ANOVA. MM data were analyzed using unsupervised clustering to identify NDD subtypes, following previous studies that classified patient subtypes based on molecular and brain functions (Williams et al. 2023; Yu et al. 2022). This unsupervised approach was selected to discover latent transdiagnostic subgroups from urinary MM profiles without stratifying the clustering by diagnosis. Moreover, consistent with previously reported approaches (Scheerer et al. 2021; Yu et al. 2022), cluster analysis was restricted to participants with NDDs, because the primary purpose of this study was to identify monoamine-based subtypes within the clinical group. TD participants were not included in the clustering step to avoid deriving clusters driven primarily by broad case-control differences; instead, they were used as an independent control group for downstream comparisons to characterize the identified subtypes. MM levels were winsorized at three standard deviations from the mean to reduce the influence of outliers and were then converted to z-scores to mitigate distance-associated issues in cluster analysis. The NbClust R package (Charrad et al. 2014) was used to determine the optimal number of clusters using the majority rule across multiple cluster validity indices (e.g., the Silhouette, Duda, and Beale indices), whereby the number of clusters supported by the largest number of indices was selected as the best-fitting solution. Accordingly, NbClust was run using the Euclidean distance and K-means method to partition NDD participants into the optimal number of clusters based on their urinary MM profiles. Furthermore, a support vector machine (SVM) with a radial basis function kernel (R packages “e1071” and “caret”) was used to assess the reliability and reproducibility of subtyping based on MM patterns. We applied five-fold cross-validation and three repeats to mitigate the potential risks of overfitting, and the procedure was repeated 100 times for a training dataset (70% of the 83 participants with NDD, n = 58) and a test dataset (30% of the 83 participants with NDD, n = 25). Model performance was assessed using accuracy, precision, recall, and F1-scores. We assessed MM patterns by comparing each identified NDD subtype with the TD group using a linear mixed-effects model (R package “lmerTest,” “MuMIn,” and “jtools”), with each MM level set as the dependent variable and group (e.g., NDD-A vs. TD) set as the independent variable. To account for non-independence of observations within families (e.g., among siblings), the family ID was modeled as a random intercept. Covariates included demographic variables (e.g., age and sex; Online Resource 2 ) that showed significant differences between each NDD subtype group and the TD group. Moreover, based on the biochemical and psychological differences between groups, we investigated the associations between MM levels and psychological characteristics using Spearman’s rank-order correlation coefficients (R package “psych”). For linear mixed-effects models, the statistical threshold was set at p < 0.05, false discovery rate (FDR)-corrected using the Benjamini-Hochberg method. Thereafter, the statistical threshold for multiple group comparisons was set at p < 0.05, family-wise error (FWE)-corrected using the Bonferroni method. For correlation analyses, the statistical threshold was set at p < 0.05, FDR-corrected using the Benjamini–Hochberg method. We evaluated cognitive features by comparing each NDD subtype group with the TD group by using a linear mixed-effects model, with each cognitive function set as the dependent variable and group set as the independent variable. Random intercepts, covariates, and statistical thresholds were the same as those employed to assess MM levels. Moreover, correlation analyses (only for MM levels and cognitive performances where group differences were significant) were conducted by calculating Spearman’s rank-order correlation coefficients. The statistical threshold was the same as that employed to assess the associations between MM levels and psychological characteristics. Image acquisition Scanning was performed using a 3T GE Signa PET/MR scanner (General Electric Healthcare, Chicago, IL, USA). Children’s heads were immobilized using a head-coil scanner (eight channels). High-resolution structural images were acquired using an axial T1-weighted magnetization-prepared rapid gradient-echo pulse sequence (TR = 8.5 ms; TE = 3.2 ms; field of view = 256 × 256; matrix size = 256 × 256; voxel size = 1 × 1 × 1 mm; 176 slices). Prior to processing, an experienced rater carefully checked the structural scans for artifacts. Image pre processing and statistical analysis The MRI data were analyzed using VBM, employing Statistical Parametric Mapping 12 (SPM12) software (Wellcome Department of Cognitive Neurology, London, UK), and surface-based morphometry, employing FreeSurfer (version 7.3.0) methods, to assess structural brain differences comprehensively. VBM quantifies regional GMVs across the whole brain, whereas surface-based morphometry focuses on the cortical surface area, reflecting cortical expansion and neurodevelopmental variations. Combining these methods enables a more detailed characterization of brain morphology. VBM (Ashburner and Friston 2000) was performed using SPM12 software running in MATLAB R2021a. This method was used to pre-process T1-weighted images and calculate the GMV across the whole brain. Structural T1-weighted images were first segmented to separate different types of tissues: gray matter, white matter, cerebrospinal fluid, soft tissue, and the skull. The images (gray matter and white matter) were then spatially normalized using diffeomorphic anatomical registration with exponentiated Lie algebra. To preserve the absolute GMV, normalized gray matter images were modulated by multiplying the Jacobian determinants derived from spatial normalization. Finally, the modulated gray matter images were smoothed with an 8-mm full-width at half-maximum Gaussian kernel. For comparison of VBM between each NDD subtype group and the TD group, we used threshold-free cluster enhancement (TFCE), which was introduced to enhance voxel-based analysis sensitivity by applying the Freedman–Lane method based on 5000 permutations (Freedman and Lane 1983; Winkler et al. 2014). The nuisance variable included total intracranial volume. The covariates were the same as employed to assess the MM levels, in addition to handedness and total intracranial volume . Moreover, an explicit mask was used to exclude noisy voxels from statistical analysis. Based on the TFCE analysis, statistical significance was set at an FWE-corrected threshold of p < 0.025 for multiple group comparisons (e.g., NDD-A vs. TD and NDD-B vs. TD). Thereafter, we identified the cluster locations obtained from the structural data using the anatomy toolbox in SPM12. Next, associations between MM levels and GMV in regions of interests (ROIs) were investigated. Based on the difference in GMVs between groups, we used MarsBaR software to extract spherical ROIs that were centered on the local maximal peaks of significant large clusters that were located within a 10-mm radius of the regions. Correlation analyses were conducted using the Spearman’s rank-order correlation coefficients. The statistical threshold was the same as that employed to assess the associations between MM levels and psychological characteristics . FreeSurfer was used to reconstruct the brain cortical surface from T1-weighted images, which automates the quantitative assessment of brain anatomy (Fischl et al. 2002). Details of the segmentation procedures have been described previously (Dale et al. 1999; Fischl et al. 2002). Finally, we used 34 regions labeled with the Desikan atlas-based classification for the cortical surface area (68 regions in total). To compare regional cortical surface areas between each NDD subtype group and the TD group, we adopted a linear mixed-effects model with each regional cortical surface area set as the dependent variable and group set as the independent variable. Random intercepts, covariates (in addition to handedness and total intracranial volume), and statistical thresholds were the same as those employed to assess the MM levels. Moreover, based on the biochemical and surface area differences between groups, we investigated the associations between MM levels and regional cortical surface areas using Spearman’s rank-order correlation coefficients. The statistical threshold was the same as that employed to assess the associations between MM levels and psychological characteristics. Results Demographics and MM pattern clustering The demographic data are shown in Table 1 . Student’s t -test showed significant differences between the TD and NDD groups in terms of age, years of education, annual household income, and intelligence quotient. Similarly, the chi-square test showed a significant difference in sex distribution between the groups. Using NbClust with the Euclidean distance and K-means method in the NDD sample (n = 83), 11 of the 26 validity indices supported a two-cluster solution. Based on the majority rule, k = 2 was selected as the optimal clustering solution. Fig. 1a presents a scatter plot of z-scored urinary MHPG, 5-HIAA, and HVA levels for NDD participants, illustrating a two-cluster solution. These NDD subtypes were named NDD-A (n = 18) and NDD-B (n = 65). To assess the reliability and reproducibility of these subtypes, the SVM algorithm with repeated cross-validation showed high accuracy (0.97 ± 0.03), precision (0.95 ± 0.09), recall (0.92 ± 0.10), and F1 scores (0.93 ± 0.07) in the test dataset ( Fig . 1b ), suggesting that urinary MM profiles can distinguish the two subtypes. The demographics and behavioral symptoms of each NDD subtype are shown in Online Resource 2 . MM data are shown in Fig . 1c . The main effect of group in the linear mixed-effects model showed that the NDD-A type had significantly higher levels of MHPG ( β = 1.80, 95% CI [1.39, 2.29], R 2 = 0.43, t = 7.99, p -FDR < 0.001, p -FWE < 0.001), 5-HIAA ( β = 0.91, 95% CI [0.36, 1.45], R 2 = 0.11, t = 3.27, p -FDR = 0.001, p -FWE = 0.002), and HVA ( β = 1.00, 95% CI [0.46, 1.54], R 2 = 0.13, t = 3.63, p -FDR < 0.001, p -FWE = 0.001) than the TD group. Moreover, the main effect of group indicated that the NDD-B type had significantly lower levels of MHPG ( β = -0.61, 95% CI [-0.97, -0.25], R 2 = 0.09, t = 3.32, p -FDR = 0.001, p -FWE = 0.003), 5-HIAA ( β = -0.57, 95% CI [-0.93, -0.21], R 2 = 0.11, t = 3.13, p -FDR = 0.002, p -FWE = 0.004), and HVA ( β = -0.65, 95% CI [-1.00, -0.30], R 2 = 0.14, t = 3.63, p -FDR = 0.001, p - FWE = 0.002) than the TD group. These results revealed distinct monoaminergic activity in the NDD subtypes, with hyperactivity in NDD-A and hypoactivity in NDD-B. Subsequently, we investigated associations between MM levels and psychological measures ( Fig . 1 d ). In the NDD-A type, MHPG levels were positively correlated with the SRS-2 total score ( p - FDR = 0.013). By contrast, the NDD-B type and TD group did not show correlations between MM levels and psychological measures. These results indicate that in the NDD-A type, elevated noradrenergic activity is linked to difficulties in social communication. Cognitive characteristics Cognitive data are shown in Fig . 2 . The main effect of group in the linear mixed-effect model showed that the NDD-B type had significantly greater values for execution time on the TMT-B ( β = 0.49, 95% CI [0.17, 0.81], R 2 = 0.36, t = 3.01, p -FDR = 0.009, p -FWE = 0.018) and TMT-Δ ( β = 0.48, 95% CI [0.14, 0.82], R 2 = 0.28, t = 2.79, p -FDR = 0.009, p -FWE = 0.018) as well as the frequency of direction errors on go trials in the stop signal task ( β = 0.65, 95% CI [0.29, 1.00], R 2 = 0.23, t = 3.61, p -FDR = 0.001, p -FWE = 0.002) than those of the TD group. These results indicates that the NDD-B type had lower levels of cognitive control, cognitive flexibility, and inhibitory control. For comparisons of each NDD subtype with the TD group, excluding the abovementioned cognitive performance aspect, see Online Resource 3 . We also investigated the correlation between MM levels and cognitive performance ( Fig . 2b ). MM levels and execution time on the TMT-B were positively correlated in the NDD-B type (MHPG: p -FDR = 0.016; 5-HIAA: p -FDR = 0.019; HVA: p - FDR = 0.016). However, the TD group did not show this correlation. These results indicate that in the NDD-B type, enhancement of monoaminergic activity is linked to slower cognitive control. Brain structural characteristics Brain structural data are shown in Fig . 3 . The main effect of group in the linear mixed-effects model showed that the NDD-A type exhibited a larger surface area in the right isthmus cingulate gyrus ( β = 1.02, 95% CI [0.49, 1.56], R 2 = 0.29, t = 3.74, p -FDR = 0.022, p -FWE = 0.044) than the TD group. However, the NDD-B type did not show significant differences in regional cortical surface areas. Additionally, whole-brain voxel-based GMV analysis using TFCE revealed a significantly larger cluster extending from the left middle frontal to the right middle frontal gyrus when comparing the NDD-B type with the TD groups (Online Resource 4). The primary peak within this cluster indicated smaller volumes in the left posterior orbital gyrus in the NDD-B type than in the TD group. Additional peaks revealed reduced volumes in the left posterior cingulate gyrus, left cuneus, left precuneus, left middle frontal gyrus, left superior occipital gyrus, right superior frontal gyrus, right supplementary motor cortex, and right posterior insula in the NDD-B type than in the TD group. In contrast, the NDD-A type did not exhibit significant differences in regional brain volumes. These results highlight the distinct patterns of structural brain differences between the NDD subtypes. Subsequently, we investigated the associations between MM levels and brain structural characteristics (where each NDD subtype showed greater surface or lesser GMV than did the TD group) ( Fig . 3b ); no correlations were found between MM levels and brain structural characteristics. Discussion This study aimed to identify urinary MM-based NDD subtypes across ADHD and ASD by using an unsupervised clustering approach and sought to examine structural brain characteristics associated with these subtypes. Cognitive performance was also examined to inform the interpretation of the identified subtypes. Clustering revealed two distinct NDD subtypes: 1) NDD-A, characterized by high MHPG, 5-HIAA, and HVA levels in urine; and 2) NDD-B, characterized by low levels of these three MMs. Although no significant differences in behavioral symptoms were observed between the two NDD subtypes, each of these demonstrated greater difficulties in social communication and sensory processing, severity of inattention and hyperactivity/impulsivity, and fatigue than did the TD group ( Online Resource 2 ). Greater difficulty in social communication was associated with higher levels of MHPG in the NDD-A subtype. Moreover, compared to the TD group, only the NDD-B type showed reduced performance in cognitive control, cognitive flexibility, and inhibitory control. Notably, compared to the TD group, the cortical surface areas were larger in the isthmus cingulate gyrus in the NDD-A type, whereas the NDD-B type exhibited smaller GMVs in the posterior orbital gyrus, posterior cingulate gyrus, superior frontal gyrus, cuneus, precuneus, supplementary motor cortex, middle frontal gyrus, superior occipital gyrus, and posterior insula. These findings suggest that the NDD-A and NDD-B subtypes, which exhibit distinct monoamine activity patterns, show distinct patterns of structural brain differences. Monoamine hyper- or hypofunction-subtype specificity We found that the urinary levels of MHPG, 5-HIAA, and HVA were higher in the NDD-A than in the TD group, supporting the hypothesis of monoaminergic hyperactivity in NDDs (Chugani et al. 1999 ; Dvoráková et al. 2007 ; Goldberg et al. 2009 ; Ulke et al. 2019 ). Notably, the NDD-A type displayed a positive correlation between urinary MHPG levels and social communication difficulties, while no significant differences in cognitive function were found between the NDD-A and TD groups. Here, a relevant concept is the inverted U-shaped relationship between monoamine levels and cognitive function (Arnsten 2009 ; Baldi and Bucherelli 2005 ), whereby high noradrenergic activity is associated with poorer social communication, but is better for maintaining cognitive performance. The noradrenergic pathway projects from the locus coeruleus to the amygdala and cingulate cortex (Heilbronner and Haber 2014 ; Joshi et al. 2016 ; Stahl 2021 ). A pharmacological study reported that, after intraperitoneal injection of N -(2-chloroethyl)- N -ethyl-2-bromobenzylamine (a selective noradrenergic neurotoxin) in mice, the reduction in noradrenergic activity of the locus coeruleus increased anxiety-related social behavior and decreased noradrenaline content in the amygdala (Itoi et al. 2011 ). This indicated a role for the locus coeruleus–amygdala noradrenergic circuit in social behavioral function. Moreover, Monk et al. ( 2010 ) revealed stronger connectivity between the amygdala and ventral anterior cingulate cortex in individuals with ASD than in TD controls when they viewed faces depicting socially relevant emotions. This suggests the involvement of atypical social information processing and heightened social sensitivity in this connectivity. Accordingly, noradrenergic hyperactivity in the locus coeruleus–cingulate cortex–amygdala pathway may contribute to social functioning difficulties in NDD-A, specifically given the association of increased urinary MHPG levels with social communication difficulties in NDD-A. Alternatively, increased serotonin levels, together with noradrenergic hyperactivity, may be related to social communication difficulties, because noradrenaline facilitates serotonin efflux by binding to the α1 receptor on serotonergic neurons (Stahl 2021 ). Previous studies have reported an association between elevated serotonin levels and social communication difficulties in ASD (Kheirouri et al. 2016 ; Yang et al. 2015 ). Our findings suggested that the interaction between the noradrenergic and serotoninergic systems may contribute to social communication impairment in NDD-A. We argue that individuals with NDD-A may be characterized as having hyperactivity of the noradrenergic rather than serotonergic system, with the former being specifically associated with social communication difficulties. The NDD-B subtype displayed lower levels of MHPG, 5-HIAA, and HVA in urine than did the TD group, supporting the hypothesis of monoaminergic hypoactivity in NDDs (Alabdali et al. 2014 ; Gevi et al. 2020 ; Gevi et al. 2016 ; Hannestad et al. 2010 ; Hellings 2023 ; Mechler et al. 2022 ; Ota et al. 2021 ). This group also demonstrated poorer cognitive control, cognitive flexibility, and inhibitory control than that in the TD group. These findings suggested that the NDD-B subtype may exhibit executive function difficulties associated with monoaminergic hypoactivity. Moreover, higher urinary MM levels were associated with reduced cognitive control in the NDD-B subtype. Since the TMT-B has also been found to be associated with frontal executive function (Chan et al. 2015 ), its results must be combined with those of other executive function measures to assess cognitive control. The serotonergic system projects from the raphe nuclei to the prefrontal cortex via the ventral tegmental area and locus coeruleus (Hensler 2006 ; Stahl 2021 ). Additionally, the prefrontal cortex receives direct projections from dopaminergic neurons in the ventral tegmental area and noradrenergic neurons in the locus coeruleus (Arnsten et al. 2015 ; Aston-Jones and Cohen 2005 ; Stahl 2021 ). Moreover, binding of serotonin to serotonin-2A receptors on noradrenergic neurons in the locus coeruleus and dopaminergic neurons in the ventral tegmental area regulates the release of noradrenaline and dopamine into the prefrontal cortex (Stahl 2021 ), highlighting the interaction between these monoamines in the prefrontal cortex. Through this modulation, monoaminergic activity may be related to executive function. Neuroimaging and pharmacological studies have reported that serotoninergic, noradrenergic, and dopaminergic activity modulates executive function, such as cognitive control and set shifting (Arnsten and Li 2005 ; Arnsten and Pliszka 2011 ; Kuwamizu et al. 2023 ; Yamashita and Yamamoto 2021 ). These findings suggest that the interaction between these monoamines and their reduced availability in the prefrontal cortex may contribute to executive function difficulties in individuals with NDD-B. Unlike those with NDD-A, individuals with NDD-B may be particularly characterized as having a monoaminergic hypoactivity disorder specifically associated with executive function impairments. These aspects of monoaminergic activity heterogeneity highlight the need for using different approaches and considerations when diagnosing and treating NDDs across the identified subtypes. Distinct brain structural anomalies in monoamine hyper- or hypofunction subtypes Structural analyses revealed significantly larger surface areas in the right isthmus cingulate gyrus in the NDD-A subtype group than in the TD group. In contrast, the NDD-B subtype displayed significantly smaller GMVs in the left posterior orbital gyrus, left posterior cingulate gyrus, right superior frontal gyrus, left cuneus, left precuneus, right supplementary motor cortex, left middle frontal gyrus, left superior occipital gyrus, and right posterior insula than those in the TD group. Thus, monoaminergic subtype specificity in NDD-A and NDD-B is associated with distinct brain structural differences. Surface area enlargement in the NDD-A subtype was observed in the right isthmus cingulate gyrus. Anatomically, the isthmus cingulate is a key structure that connects the posterior cingulate cortex to the parahippocampal gyrus (Caeyenberghs et al. 2016 ; Chen et al. 2021 ). Although not well understood, the isthmus cingulate gyrus may be involved in emotional regulation and episodic memory processing, based on its association with anatomical connections (Weerasekera et al. 2024 ). Interestingly, a greater surface area in the isthmus cingulate gyrus has been associated with worse social ability scores in individuals with ASD (Doyle-Thomas et al. 2013 ), suggesting the involvement of atypical isthmus cingulate gyrus morphometry in social behavioral impairments in NDDs. Studies on ASD have shown associations between an increase in cortical surface area and cerebral cortex overgrowth (Donovan and Basson 2017 ; Ohta et al. 2016 ), suggesting that an increased cortical surface area, possibly linked to abnormal gyrification (Kitajima et al. 2023 ), may lead to neuronal overgeneration and abnormal neural connections (Hogstrom et al. 2013 ; Meyer et al. 2014 ). Furthermore, the cingulate cortex, including the isthmus cingulate gyrus, is a key target of the ascending noradrenergic pathway originating from the locus coeruleus (Heilbronner and Haber 2014 ; Joshi et al. 2016 ; Stahl 2021 ), suggesting that the noradrenergic activity in the locus coeruleus–cingulate cortex circuit, together with the amygdala, may affect social functional impairments through its modulation of emotional regulation (Itoi et al. 2011 ; Monk et al. 2010 ; Terbeck et al. 2016 ). Our findings suggest that surface area enlargement in the isthmus cingulate gyrus may be a key factor in the social communication difficulties observed in individuals with the NDD-A subtype, potentially due to alterations in noradrenergic neural connections. We demonstrated GMV reductions in the posterior orbital gyrus, posterior cingulate gyrus, superior frontal gyrus, precuneus, supplementary motor cortex, and middle frontal gyrus of the NDD-B subtype group as compared to the TD group. The superior frontal gyrus, middle frontal gyrus, and supplementary motor cortex correspond to executive functions (Deng et al. 2023 ; Duan et al. 2018 ; Sjöberg et al. 2019 ). Although not fully understood, the posterior orbital gyrus is located within the lateral orbitofrontal cortex and may play a role in executive function processes, together with the frontal areas (Deng et al. 2017 ; Rolls 2019 ). Functional connections in the orbitofrontal area, such that cognitive control and inhibitory control are represented predominantly in the frontoparietal network, have been proposed previously (Lückmann et al. 2014 ; Tozzi et al. 2020 ). As differences in the structural and functional connectivity of the orbitofrontal area have been implicated in ADHD (Li et al. 2015 ; Yang et al. 2018b ), these frontal areas and their association with these networks may underlie the functional architecture of executive function differences in NDDs. Furthermore, the precuneus and posterior cingulate cortex comprise the functional core of the default mode network (Utevsky et al. 2014 ; Wang et al. 2006 ), indicating that suppression of this network may be associated with a reduction in cognitive control and inhibitory control (Ishihara et al. 2021 ). GMV reductions in these frontal–precuneus–posterior cingulate areas observed in NDD-B may be associated with executive function difficulties through differences in structural and functional connectivity, such as in the frontoparietal and default mode networks. At the molecular level, a previous study reported that Nagase analbuminemic rats, an animal model of NDD, showed lower serotonin levels in the prefrontal cortex (Hakamada and Yamamoto 2014 ), suggesting the importance of serotonin synthesis in NDDs. Moreover, serotonin facilitates noradrenaline and dopamine efflux via serotonin-2A receptor activity in the frontal cortex (Bortolozzi et al. 2005 ; Stahl 2021 ). Furthermore, van der Meer et al. (2015) reported that S-allele carriers with abnormal serotonin transporter expression have smaller GMVs in the frontal lobe, noting a correlation between frontal GMV and ADHD symptoms, suggesting the involvement of serotonin dysregulation in ADHD severity. These aspects of excessive serotonin may contribute to structural changes in the frontal–precuneus–posterior cingulate loci in NDD-B via noradrenaline and dopamine enhancement. Consequently, GMV reductions in these regions may lead to difficulties in executive function in individuals with NDD-B. Our results also showed that the NDD-B type had smaller volumes in the cuneus, superior occipital gyrus, and posterior insula than the TD group. The cuneus and superior occipital gyrus are important for visual processing and spatial attention (Astafiev et al. 2004 ). Atypical functional connectivity between the superior occipital gyrus and motor cortex may contribute to visual-motor dysfunction in children with ASD (Nebel et al. 2016 ). A meta-analytic connectivity modeling study found coactivation of the posterior insula in sensorimotor and interoceptive processing, and language-related functions (Chang et al. 2013 ). Although further investigation is needed to clarify the functional implications, the GMV reductions in these regions may suggest that individuals with NDD-B could exhibit visual-attention, sensory-integration, and language-processing difficulties. Clinical implications and limitations The National Institute of Mental Health in the United States launched the Research Domain Criteria projects to establish a novel framework for pathophysiological research (Insel and Cuthbert 2015 ). Consequently, the present study aimed to study urinary MM patterns categorized by unsupervised machine learning and brain structural characteristics in individuals with ADHD and/or ASD. Despite clear differences in MMs, behavioral symptoms did not differ between NDD subtype groups; these could possibly be caused by other molecular and neural mechanisms. Additionally, our findings highlight the importance of personalized therapeutic strategies and reconsideration of pharmacological intervention. As NDD-A is characterized by monoaminergic hyperactivity, methylphenidate, atomoxetine, and serotonin reuptake inhibitors may not be suitable for such patients. Alternatively, a treatment option is branched-chain amino acids (BCAAs), given their association with regulation of serotonin and its precursor tryptophan (Yamashita 2020 ). As BCAAs share the system L transporter located on the surface of the blood–brain barrier with tryptophan, they may reduce excessive serotonin synthesis by regulating tryptophan availability in the brain (Yamashita 2020 ). Because BCAAs are naturally occurring amino acids found in food and supplements, they may offer a safe and non-pharmacological alternative for managing monoaminergic dysregulation in NDD-A. Our study had some limitations. First, our clustering results might be considered preliminary due to the small sample size for NDD-A, which limited the statistical power and generalizability of the results of the study. Future studies with large cohorts are warranted to validate our findings. Second, our study was cross-sectional; thus, future longitudinal studies are needed. Conclusions In summary, we reclassified children with ADHD and ASD into NDD categories based on new phenotypes using noninvasive urinary MM levels and analyzed their neurobiological characteristics after considering various confounding factors. Such an approach has not been described in this field previously. Our results demonstrated that monoaminergic hyper- or hypofunctional specificity in individuals with NDD-A and NDD-B, respectively, distinct brain structural anomalies. Our study presents a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps in ADHD and ASD. Declarations Compliance with Ethical Standards Conflict of Interest The authors have no relevant financial or non-financial interests to disclose. Ethics Approval The Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061). The procedures complied with the principles of the Declaration of Helsinki. Consent to Participate All parents provided written informed consent and all children provided assent for participation. Data Availability The data will be made available via the Child Developmental MRI (CDM) Project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a brief research proposal along with evidence of approval from the requester’s institutional review board. Funding This work was supported by a Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (grant numbers: 23K12814 to Masatoshi Yamashita), a grant from Taiju Life Social Welfare Foundation (award year 2023 and 2025 to Masatoshi Yamashita), Life Science Innovation Center (grant number: LSI24101 to Masatoshi Yamashita), the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (award year 2022 to Yoshifumi Mizuno), and Research Grants from the University of Fukui (academic years 2022, 2023, and 2024 to Yoshifumi Mizuno). These funding sources were not involved in the study design or implementation; the collection, analysis, or interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. 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Yang Q, Huang P, Li C, Fang P, Zhao N, Nan J, Wang B, Gao W, Cui LB (2018a) Mapping alterations of gray matter volume and white matter integrity in children with autism spectrum disorder: evidence from fMRI findings. Neuroreport 29: 1188-1192. Yang Z, Li H, Tu W, Wang S, Ren Y, Yi Y, Wu T, Jiang K, Shen H, Wu J, Dong X (2018b) Altered patterns of resting-state functional connectivity between the caudate and other brain regions in medication-naïve children with attention deficit hyperactivity disorder. Clin Imaging 47: 47-51. Yu W, Wang Q, Ge M, Shi X (2022) Cluster analysis of lymphocyte subset from peripheral blood in newly diagnosed idiopathic aplastic anaemia patients. Ann Med 54: 2431-2439. Zhang M, Huang Y, Jiao J, Yuan D, Hu X, Yang P, Zhang R, Wen L, Situ M, Cai J, Sun X, Guo K, Huang X (2022) Transdiagnostic symptom subtypes across autism spectrum disorders and attention deficit hyperactivity disorder: validated by measures of neurocognition and structural connectivity. BMC Psychiatry 22: 102. Zhao Y, Cui D, Lu W, Li H, Zhang H, Qiu J (2020) Aberrant gray matter volumes and functional connectivity in adolescent patients with ADHD. J Magn Reson Imaging 51: 719-726. Table Table 1 . Demographics of the typical development and neurodevelopmental disorder groups Characteristic TD (n = 83) NDD (n = 83) P -value Age (years) 10.76 (2.97) 11.75 (3.13) 0.002 Education (years) 6.19 (2.96) 7.27 (3.06) 0.001 Sex (n) Male Female 39 44 74 9 < 0.001 Annual household income (JPY) (n) < 3 000 000 3 000 000–5 000 000 5 000 000–7 000 000 ≥ 7 000 000 6 9 20 48 10 24 17 32 0.002 Diagnosis (n) ADHD ASD ADHD+ASD NA 21 21 41 NA Handedness (n) Right Left Ambidextrous 78 3 2 75 8 0 0.147 Intelligence quotient (score) 106.28 (12.77) 96.67 (12.75) < 0.001 Parameters are indicated as the mean ( SD ) or n. P -values for age, education, income, and intelligence quotient are based on t -tests for the comparison of the TD group with each NDD subtype. P -values for sex ratio and handedness ratio are based on C hi-square tests for the comparison of TD with each NDD subtype. ADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; NA, not applicable; NDD, neurodevelopmental disorder; SD , standard deviation; TD, typical development. Additional Declarations No competing interests reported. Supplementary Files ESM1.docx ESM2.docx ESM4.docx ESM3.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 04 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 29 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8477091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570795247,"identity":"95f3e1a4-ae43-4816-b451-500e454495bd","order_by":0,"name":"Masatoshi 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06:11:35","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":229964,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/36a98a7ebd490d0b5a832434.html"},{"id":100011783,"identity":"0afb20b7-42e6-4b4c-a8ef-a08fb4070b56","added_by":"auto","created_at":"2026-01-12 06:11:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281347,"visible":true,"origin":"","legend":"\u003cp\u003eNeurodevelopmental disorder subtypes based on urinary monoamine metabolite patterns.\u003c/p\u003e\n\u003cp\u003e(a) Using NbClust with the Euclidean distance and K-means method, a two-cluster solution was selected by majority rule across cluster validity indices. The scatter plot shows z-scored urinary MHPG, 5-HIAA, and HVA levels, colored by cluster assignment. (b) The SVM algorithm with repeated cross-validation, designed to mitigate the potential risk of overfitting in the standalone SVM, showed high accuracy and F1 scores. (c) Based on FDR- and FWE-corrected thresholds (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05), the NDD-A type showed higher MHPG, 5-HIAA, and HVA levels than those of the TD group. The NDD-B type showed lower MHPG, 5-HIAA, and HVA levels than those of the TD group. (d) In the associations between monoamine metabolite levels and behavioral symptoms, the NDD-A type displayed a positive correlation between MHPG levels and SRS-2 total score. In the NDD-B type and TD group, such correlations were not significant. *** \u003cem\u003ep\u003c/em\u003e-FWE \u0026lt; 0.001, ** \u003cem\u003ep\u003c/em\u003e-FWE-\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, *´ \u003cem\u003ep\u003c/em\u003e-FDR \u0026lt; 0.05. 5-HIAA, 5-hydroxyindoleacetic acid; CHR, chronotype; FAT, fatigue; FDR, false discovery rate; FWE, family-wise error; HVA, homovanillic acid; MHPG, 4-hydroxy-3-methoxyphenylglycol; NDD, neurodevelopmental disorder; SSP, Short Sensory Profile; SRS, Social Responsiveness Scale; SVM, support vector machine; TD, typical development.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/b8b3a378f6ea9de172ea8a8a.png"},{"id":100361940,"identity":"e3344e71-6ea0-4049-8852-3e04f6860b29","added_by":"auto","created_at":"2026-01-16 07:45:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151377,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive differences between the groups.\u003c/p\u003e\n\u003cp\u003e(a) Based on FDR- and FWE-corrected thresholds (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), the NDD-B type showed worse performance in the TMT-B, TMT-Δ, and direction errors on the go trial of the stop signal task as compared to the TD group. (b) In the NDD-B type, levels of MHPG, 5-HIAA, and HVA were positively correlated with the time taken to complete the TMT-B. In contrast, these correlations with monoamine metabolites were not significant in the TD group. ** \u003cem\u003ep\u003c/em\u003e-FWE \u0026lt; 0.01, *´ \u003cem\u003ep\u003c/em\u003e-FDR \u0026lt; 0.05. 5-HIAA, 5-hydroxyindoleacetic acid; FDR, false discovery rate; FWE, family-wise error; HVA, homovanillic acid; MHPG, 4-hydroxy-3-methoxyphenylglycol; NDD, neurodevelopmental disorder; TD, typical development; TMT, Trail-Making Test.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/ed547c204d092e616d72fd93.png"},{"id":100361625,"identity":"1f74a1eb-2179-4791-924a-51069c7de2df","added_by":"auto","created_at":"2026-01-16 07:45:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":446690,"visible":true,"origin":"","legend":"\u003cp\u003eStructural brain differences among the groups\u003c/p\u003e\n\u003cp\u003e(a) The NDD-A type showed a larger surface area in the right isthmus cingulate gyrus than that in the TD group. The NDD-B type showed smaller volumes in the left posterior orbital gyrus, left posterior cingulate gyrus, left cuneus, left precuneus, left middle frontal gyrus, left superior occipital gyrus, right superior frontal gyrus, right supplementary motor cortex, and right posterior insula than those in the TD group. (b) Using ROIs (where each subtype showed a greater surface area or lesser GMV than that in the TD group), correlations between monoamine metabolites and brain structure were not significant. 5-HIAA, 5-hydroxyindoleacetic acid; Cu, cuneus; GMV, gray matter volume; HVA, homovanillic acid; ICG, isthmus cingulate gyrus; L, left; MFG, middle frontal gyrus; MHPG, 4-hydroxy-3-methoxyphenylglycol; NDD, neurodevelopmental disorder; PCG, posterior cingulate gyrus; PCu, precuneus; PINS, posterior insula; POG, posterior orbital gyrus; R, right; SFG, superior frontal gyrus; SMC, supplementary motor cortex; SOG, superior occipital gyrus; TD, typical development, TFCE, threshold-free cluster enhancement.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/f7ea2c735001d074e5bdfdf8.png"},{"id":100380956,"identity":"cb3c881e-f743-4167-9a13-e4b3484a9e2b","added_by":"auto","created_at":"2026-01-16 10:36:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1784934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/90bb0945-3d4b-42fa-bb7c-cc7c63f7ced7.pdf"},{"id":100361642,"identity":"87864639-0498-47ca-99fb-3b7b70d44d7e","added_by":"auto","created_at":"2026-01-16 07:45:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33049,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/2a8efeaa68e457347d165c8c.docx"},{"id":100011785,"identity":"f6de1d79-25c4-4d54-91c4-3cb54ce76ce3","added_by":"auto","created_at":"2026-01-12 06:11:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29098,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/61b37ffb8100bdd85fab1036.docx"},{"id":100011793,"identity":"9730e39c-84ee-4f53-acff-41b98cbe932b","added_by":"auto","created_at":"2026-01-12 06:11:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26613,"visible":true,"origin":"","legend":"","description":"","filename":"ESM4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/5be76e1479e13c8d767500b3.docx"},{"id":100011796,"identity":"fbec0aaa-793b-479a-9ae4-5adeebd695e1","added_by":"auto","created_at":"2026-01-12 06:11:35","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":161953,"visible":true,"origin":"","legend":"","description":"","filename":"ESM3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8477091/v1/b81df7f89d4bee69db70a8e8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transdiagnostic monoamine-based subtyping for attention- deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are common neurodevelopmental disorders (NDDs) in children and adolescents, with prevalence rates of \u0026gt;\u0026thinsp;5% and \u0026gt;\u0026thinsp;1.5% (Baio et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), respectively. ADHD symptoms include inattention, hyperactivity, and impulsivity, whereas ASD is characterized by difficulties in social communication and interaction, restricted and repetitive behavioral patterns, and atypical sensory responses (Lane et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides diagnostic criteria for these conditions, the symptoms of children with ADHD and ASD may not fit within the boundaries of a single disorder, as considerable clinical overlap (Grzadzinski et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a high comorbidity rate (Simonoff et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and within-disorder heterogeneity (Yamashita et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have been observed. Additionally, ADHD and ASD are associated with executive function differences (Townes et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), increased likelihood of various psychiatric disorders, fatigue, and sleep disorder (Antshel et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Axelsson et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), highlighting the neurobiological overlap. As ADHD and ASD may be viewed as different manifestations of the same overarching disorder (Antshel and Russo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), establishing NDD phenotypes based on biomarkers, such as magnetic resonance imaging (MRI) parameters and biochemical indices, is important for biologically informed classifications.\u003c/p\u003e \u003cp\u003ePrevious MRI studies have reported that, compared to typically developing (TD) individuals, individuals with ADHD show decreased gray matter volumes (GMVs) in the frontal regions, precuneus, and basal ganglia (Moreno-Alc\u0026aacute;zar et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vilgis et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). GMV reductions in the frontal-temporal regions, precuneus, amygdala, and cerebellum have also been reported in individuals with ASD compared to TD individuals (Li et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sato et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Nonetheless, such reductions have not been reported in other studies (Bonilha et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Seidman et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Semrud-Clikeman et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These inconsistent results may reflect the phenotypic heterogeneity of NDDs. Additionally, several studies have reported that ADHD and ASD share overlapping brain structural characteristics in regions such as the medial temporal lobe, inferior parietal cortex, and cerebellum (Brieber et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dougherty et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, these studies on brain structure have also not always yielded consistent results. Hence, while clinical guidelines exist for ADHD pharmacotherapy, clear biomarker-based criteria to guide individualized medication selection, particularly for individuals with co-occurring ADHD and ASD, remain limited, and definitive biomarkers for diagnosis have yet to be established.\u003c/p\u003e \u003cp\u003eMonoamines represent a candidate biomarker, given its association with the pharmacological targets of therapeutic drugs for ADHD and ASD, such as methylphenidate, atomoxetine, and risperidone (Hellings \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mechler et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ota et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous neuroimaging and pharmacological studies have reported that individuals with ADHD show decreased dopamine and noradrenaline and increased serotonin levels compared to control groups (Hannestad et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nikolaus et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Parlatini et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, reduced dopamine and increased serotonin levels have been reported in individuals with ASD compared to control groups (Brandenburg et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chugani et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Goldberg et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Although these findings suggest that complex changes between monoamines are involved in the molecular mechanisms underlying these NDDs, the evidence has been inconsistent (Alabdali et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Karlsson et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Schalbroeck et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ulke et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wiers et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may be due to the diversity within these disorders as well as their neurobiological overlap, as ADHD and ASD are not single conditions, but rather represent an aggregate of heterogeneous mechanisms. In this context, investigating peripheral monoaminergic indices may help capture biologically relevant inter-individual variability. Additionally, ADHD and ASD have been associated with alterations in the peripheral monoaminergic system, as indicated by changes in the urinary excretion of monoamines and their metabolites, such as 4-hydroxy-3-methoxyphenylglycol (MHPG, a noradrenaline metabolite), 5-hydroxyindoleacetic acid (5-HIAA, a serotonin metabolite), and homovanillic acid (HVA, a dopamine metabolite) (Dvor\u0026aacute;kov\u0026aacute; et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gevi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gevi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As urinary monoamine metabolites (MMs) are transported from the brain via the organic anion transporter 3 system in the blood\u0026ndash;brain barrier (Mori et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ohtsuki et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), they may serve as valuable noninvasive biomarkers of monoamine activity in the brain (Amin et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Yamashita and Yamamoto \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Elucidating the brain structural characteristics associated with urinary MM subtypes of NDD may help to address the inconsistent findings associated with within-disorder heterogeneity and cross-disorder phenotypic overlap. Nevertheless, investigations of the brain structure of NDD subtypes to date have been limited to clinical symptom- and executive function-related subtypes (Cordova et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and little is known about NDD subtypes classified by urinary MM patterns.\u003c/p\u003e \u003cp\u003eWe theorized that a better understanding of within-disorder heterogeneity and cross-disorder overlap may be helpful when considering prognosis or informing treatment planning. Thus, this study aimed to clarify this gap in knowledge by investigating whether transdiagnostic ADHD and ASD subtypes, defined by urinary MM profiles, would show differences in brain structure. We hypothesized that, when using an unsupervised machine learning approach, children with these NDDs would be categorized into at least two clusters based on urinary levels of MHPG, 5-HIAA, and HVA. We further hypothesized that these monoamine-based subtypes would show distinguishable brain structural profiles (e.g., regional GMV and cortical surface area) as compared to TD children, and that these structural profiles would differ between NDD subtypes. We also examined cognitive performance to support the interpretation and characterization of the identified subtypes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eParticipants\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061)\u003c/span\u003e and the study procedures complied with the principles of the Declaration of Helsinki. All \u003cspan lang=\"EN-US\"\u003eparents\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;provided\u0026nbsp;\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003ewritten\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;informed consent, and\u0026nbsp;\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eall children provided assent\u003c/span\u003e for participation. The study involved 172 children (TD group: n = 86; NDD group: n = 86; age: 6\u0026minus;17 years). Children who were diagnosed with ADHD, ASD, or co-occurring\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;diagnoses based on the DSM-5 were recruited through a hospital at the University of Fukui, Japan. TD children who did not receive special support education were recruited from the local community. All participants had a Wechsler full-scale intelligence quotient score \u0026ge; 70 (\u003cstrong\u003eTable 1\u003c/strong\u003e); no history of neurological, cardiovascular, or psychiatric illness; no contraindication for MRI; and had discontinued medication based on its half-life prior to the experiment. Data from three TD children were excluded from the analysis because of claustrophobia during the MRI scan. Furthermore, data from three children with NDDs were excluded because of physical deconditioning and claustrophobia during the MRI scan. Thus, data from 83 TD children and 83 children with NDDs were used in the final analysis (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePsychological \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eand cognitive \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003emeasurements\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe psychological questionnaire targeted the Japanese version of the Social Responsiveness Scale (SRS-2), Short Sensory Profile, Conners 3 Scale, Chalder Fatigue Scale, and Munich Chronotype Questionnaire.\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;Furthermore, cognitive tests included the stop signal and spatial working memory tasks from the Cambridge Neuropsychological Tasks Automated Battery and parts A and B of the Trail-Making Test (TMT). For the details of each questionnaire and cognitive function task, see \u003cstrong\u003eOnline Resource 1\u003c/strong\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eDetermination of MMs\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to testing, the children were instructed to refrain from intense physical activity and certain foods and beverages, such as alcohol, coffee, high-fat fish, red meat, and blue cheese, which can be difficult to digest, for 24 h (Yamashita and Yamamoto 2021). On the test day, fresh urine was collected after the children had been allowed to rest for 30 min. Urine samples were diluted with 6.7 mM hydrochloric acid and 2.5% perchloric acid to separate albumin, as previously reported (Yamashita and Yamamoto 2021)\u003cspan lang=\"EN-US\"\u003e. Obtained supernatants were stored at -80 \u0026deg;C until high-performance liquid chromatography (HPLC) was performed.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUrinary MHPG, 5-HIAA, and HVA (Sigma-Aldrich Inc., St Louis, MO, USA) content was measured using an HPLC device with an electrochemical detector (Nanospace SI-2 3005, Osaka Soda Corporation, Osaka, Japan) and a chromate recorder (C-R8A; Shimadzu Corporation, Kyoto, Japan). The mobile phase consisted of 15% methanol in a solution (pH 4.13) containing 30 mM citric acid, 10 mM disodium hydrogen phosphate, 0.5 mM sodium octyl sulfate, 50 mM sodium chloride, and 0.05 mM ethylenediaminetetraacetic acid, as previously reported (Yamashita and Yamamoto 2017; 2021)\u003cspan lang=\"EN-US\"\u003e. This was pumped through a 5-\u0026mu;M C\u003csub\u003e18\u003c/sub\u003e column (150 mm \u0026times; 4.6 mm; TSK gel, ODS-80TM, Tosoh, Tokyo, Japan) at a flow rate of 0.7 mL/min on a single pump (Nanospace SI-2 3101; Osaka Soda Corp., Osaka, Japan) and temperature of 25\u0026deg;C in the column oven (Nanospace SI-I 2004; Shiseido Corporation, Tokyo, Japan). MHPG, 5-HIAA, and HVA levels were detected electrochemically (voltage: 700 mV) with retention times of approximately 6, 12, and 18 min, respectively.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eStatistical analysis of demographic, psychological, cognitive, and MMs data\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eStatistical analyses were conducted using R (version 4.3.0; The R Foundation for Statistical Computing, Vienna, Austria), except for voxel-based morphometry (VBM) statistical analysis. All data were initially assessed for normality and homogeneity of variance, and statistical tests were determined accordingly. Differences between groups based on demographic and behavioral symptoms data were analyzed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, Welch\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test, Chi-square test, one-way analysis of variance (ANOVA), and Welch\u0026rsquo;s one-way ANOVA.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMM data were analyzed using unsupervised clustering to identify NDD subtypes, following previous studies that classified patient subtypes based on molecular and brain functions (Williams et al. 2023; Yu et al. 2022). This unsupervised approach was selected to discover latent transdiagnostic subgroups from urinary MM profiles without stratifying the clustering by diagnosis. Moreover, consistent with previously reported approaches (Scheerer et al. 2021; Yu et al. 2022), cluster analysis was restricted to participants with NDDs, because the primary purpose of this study was to identify monoamine-based subtypes within the clinical group. TD participants were not included in the clustering step to avoid deriving clusters driven primarily by broad case-control differences; instead, they were used as an independent control group for downstream comparisons to characterize the identified subtypes. MM levels were winsorized at three standard deviations from the mean to reduce the influence of outliers and were then converted to z-scores to mitigate distance-associated issues in cluster analysis. The NbClust R package (Charrad et al. 2014)\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;was used to determine the optimal number of clusters using the majority rule across multiple cluster validity indices (e.g., the Silhouette, Duda, and Beale indices), whereby the number of clusters supported by the largest number of indices was selected as the best-fitting solution. Accordingly, NbClust was run using the Euclidean distance and K-means method to partition NDD participants into the optimal number of clusters based on their urinary MM profiles. Furthermore, a support vector machine (SVM) with a radial basis function kernel (R packages \u0026ldquo;e1071\u0026rdquo; and \u0026ldquo;caret\u0026rdquo;) was used to assess the reliability and reproducibility of subtyping based on MM patterns. We applied five-fold cross-validation and three repeats to mitigate the potential risks of overfitting, and the procedure was repeated 100 times for a training dataset (70% of the 83 participants with NDD, n = 58) and a test dataset (30% of the 83 participants with NDD, n = 25). Model performance was assessed using accuracy, precision, recall, and F1-scores.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWe assessed MM patterns by comparing each identified NDD subtype with the TD group using a linear mixed-effects model (R package \u0026ldquo;lmerTest,\u0026rdquo; \u0026ldquo;MuMIn,\u0026rdquo; and \u0026ldquo;jtools\u0026rdquo;), with each MM level set as the dependent variable and group (e.g., NDD-A vs. TD) set as the independent variable. To account for non-independence of observations within families (e.g., among siblings), the family ID was modeled as a random intercept. Covariates included demographic variables (e.g., age and sex; \u003cstrong\u003eOnline Resource 2\u003c/strong\u003e) that showed significant differences between each NDD subtype group and the TD group. Moreover, based on the biochemical and psychological differences between groups, we investigated the associations between MM levels and psychological characteristics using Spearman\u0026rsquo;s rank-order correlation coefficients (R package \u0026ldquo;psych\u0026rdquo;). For linear mixed-effects models, the statistical threshold was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, false discovery rate (FDR)-corrected using the Benjamini-Hochberg method. Thereafter, the statistical threshold for multiple group comparisons was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, family-wise error (FWE)-corrected using the Bonferroni method. For correlation analyses, the statistical threshold was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, FDR-corrected using the Benjamini\u0026ndash;Hochberg method.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;We evaluated cognitive features by comparing each NDD subtype group with the TD group by using a linear mixed-effects model, with each cognitive function set as the dependent variable and group set as the independent variable. Random intercepts, covariates, and statistical thresholds were the same as those employed to assess MM levels. Moreover, correlation analyses (only for MM levels and cognitive performances where group differences were significant) were conducted by calculating Spearman\u0026rsquo;s rank-order correlation coefficients. The statistical threshold was the same as that employed to assess the associations between MM levels and psychological characteristics.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eImage acquisition\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eScanning was performed using a 3T GE Signa PET/MR scanner (General Electric Healthcare, Chicago, IL, USA). Children\u0026rsquo;s heads were immobilized using a head-coil scanner (eight channels). High-resolution structural images were acquired using an axial T1-weighted magnetization-prepared rapid gradient-echo pulse sequence (TR = 8.5 ms; TE = 3.2 ms; field of view = 256 \u0026times; 256; matrix size = 256 \u0026times; 256; voxel size = 1 \u0026times; 1 \u0026times; 1 mm; 176 slices). Prior to processing, an experienced rater carefully checked the structural scans for artifacts.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eImage\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003epre\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eprocessing\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e and statistical analysis\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MRI data were analyzed using VBM, employing Statistical Parametric Mapping 12 (SPM12) software (Wellcome Department of Cognitive Neurology, London, UK), and surface-based morphometry, employing FreeSurfer (version 7.3.0) methods, to assess structural brain differences comprehensively. VBM quantifies regional GMVs across the whole brain, whereas\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;surface-based morphometry focuses on the cortical surface area, reflecting cortical expansion and neurodevelopmental variations. Combining these methods enables a more detailed characterization of brain morphology.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eVBM (Ashburner and Friston 2000) was performed using SPM12 software running in MATLAB R2021a. This method was used to pre-process T1-weighted images and calculate the GMV across the whole brain. Structural T1-weighted images were first segmented to separate different types of tissues: gray matter, white matter, cerebrospinal fluid, soft tissue, and the skull. The images (gray matter and white matter) were then spatially normalized using diffeomorphic anatomical registration with exponentiated Lie algebra. To preserve the absolute GMV, normalized gray matter images were modulated by multiplying the Jacobian determinants derived from spatial normalization. Finally, the modulated gray matter images were smoothed with an 8-mm full-width at half-maximum Gaussian kernel. For comparison of VBM between each NDD subtype group and the TD group, we used threshold-free cluster enhancement (TFCE), which was introduced to enhance voxel-based analysis sensitivity by applying the Freedman\u0026ndash;Lane method based on 5000 permutations (Freedman and Lane 1983; Winkler et al. 2014). The nuisance variable included total intracranial volume. The covariates were the same as employed to assess the MM levels, in addition to handedness and total intracranial volume\u003cspan lang=\"EN-US\"\u003e. Moreover, an explicit mask was used to exclude noisy voxels from statistical analysis. Based on the TFCE analysis, statistical significance was set at an FWE-corrected threshold of \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.025 for multiple group comparisons (e.g., NDD-A vs. TD and NDD-B vs. TD). Thereafter, we identified the cluster locations obtained from the structural data using the anatomy toolbox in SPM12.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eNext, associations between MM levels and GMV in regions of interests (ROIs) were investigated. Based on the difference in GMVs between groups, we used MarsBaR software to extract spherical ROIs that were centered on the local maximal peaks of significant large clusters that were located within a 10-mm radius of the regions. Correlation analyses were conducted using the Spearman\u0026rsquo;s rank-order correlation coefficients. The statistical threshold was\u0026nbsp;\u003cspan lang=\"EN-US\"\u003ethe same as that employed to assess the associations between MM levels and psychological characteristics\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFreeSurfer was used to reconstruct the brain cortical surface from T1-weighted images, which automates the quantitative assessment of brain anatomy (Fischl et al. 2002). Details of the segmentation procedures have been described previously (Dale et al. 1999; Fischl et al. 2002). Finally, we used 34 regions labeled with the Desikan atlas-based classification for the cortical surface area (68 regions in total).\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;To compare regional cortical surface areas between each NDD subtype group and the TD group, we adopted a linear mixed-effects model with each regional cortical surface area set as the dependent variable and group set as the independent variable. Random intercepts, covariates (in addition to handedness and total intracranial volume), and statistical thresholds were the same as those employed to assess the MM levels. Moreover, based on the biochemical and surface area differences between groups, we investigated the associations between MM levels and regional cortical surface areas using Spearman\u0026rsquo;s rank-order correlation coefficients. The statistical threshold was the same as that employed to assess the associations between MM levels and psychological characteristics.\u003c/span\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eDemographics and MM pattern clustering\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe demographic data are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test showed significant differences between the TD and NDD groups in terms of age, years of education, annual household income, and intelligence quotient. Similarly, the chi-square test showed a significant difference in sex distribution between the groups.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUsing NbClust with the Euclidean distance and K-means method in the NDD sample (n = 83), 11 of the 26 validity indices supported a two-cluster solution. Based on the majority rule, k = 2 was selected as the optimal clustering solution. \u003cstrong\u003eFig. 1a\u003c/strong\u003e presents a scatter plot of z-scored urinary MHPG, 5-HIAA, and HVA levels for NDD participants, illustrating a two-cluster solution. These NDD subtypes were named NDD-A (n = 18) and NDD-B (n = 65). To assess the reliability and reproducibility of these subtypes, the SVM algorithm with repeated cross-validation showed high accuracy (0.97 \u0026plusmn; 0.03), precision (0.95 \u0026plusmn; 0.09), recall (0.92 \u0026plusmn; 0.10), and F1 scores (0.93 \u0026plusmn; 0.07) in the test dataset (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 1b\u003c/span\u003e\u003c/strong\u003e), suggesting that urinary MM profiles can distinguish the two subtypes. The demographics and behavioral symptoms of each NDD subtype are shown in \u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eOnline Resource 2\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eMM data are shown in \u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 1c\u003c/span\u003e\u003c/strong\u003e. The main effect of group in the linear mixed-effects model showed that the NDD-A type had significantly higher levels of MHPG (\u003cem\u003e\u0026beta;\u003c/em\u003e = 1.80, 95% CI [1.39, 2.29], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.43, \u003cem\u003et\u003c/em\u003e = 7.99, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR \u0026lt; 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE \u0026lt; 0.001), 5-HIAA (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.91, 95% CI [0.36, 1.45], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.11, \u003cem\u003et\u003c/em\u003e = 3.27, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.002), and HVA (\u003cem\u003e\u0026beta;\u003c/em\u003e = 1.00, 95% CI [0.46, 1.54], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.13, \u003cem\u003et\u003c/em\u003e = 3.63, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR \u0026lt; 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.001) than the TD group. Moreover, the main effect of group indicated that the NDD-B type had significantly lower levels of MHPG (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.61, 95% CI [-0.97, -0.25], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.09, \u003cem\u003et\u003c/em\u003e = 3.32, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.003), 5-HIAA (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.57, 95% CI [-0.93, -0.21], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.11, \u003cem\u003et\u003c/em\u003e = 3.13, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.002, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.004), and HVA (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.65, 95% CI [-1.00, -0.30], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.14, \u003cem\u003et\u003c/em\u003e = 3.63, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-\u003cspan lang=\"EN-US\"\u003eFWE = 0.002) than the TD group. These results revealed distinct monoaminergic activity in the NDD subtypes, with hyperactivity in NDD-A and hypoactivity in NDD-B.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSubsequently, we investigated associations between MM levels and psychological measures (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 1\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ed\u003c/span\u003e\u003c/strong\u003e). In the NDD-A type, MHPG levels were positively correlated with the SRS-2 total score (\u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-\u003cspan lang=\"EN-US\"\u003eFDR = 0.013). By contrast, the NDD-B type and TD group did not show correlations between MM levels and psychological measures. These results indicate that in the NDD-A type, elevated noradrenergic activity is linked to difficulties in social communication.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eCognitive characteristics\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive data are shown in \u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 2\u003c/span\u003e\u003c/strong\u003e. The main effect of group in the linear mixed-effect model showed that the NDD-B type had significantly greater values for execution time on the TMT-B (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.49, 95% CI [0.17, 0.81], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.36, \u003cem\u003et\u003c/em\u003e = 3.01, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.009, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.018) and TMT-\u0026Delta; (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.48, 95% CI [0.14, 0.82], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.28, \u003cem\u003et\u003c/em\u003e = 2.79, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.009, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.018) as well as the frequency of direction errors on go trials in the stop signal task (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.65, 95% CI [0.29, 1.00], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.23, \u003cem\u003et\u003c/em\u003e = 3.61, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.001, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.002) than those of the TD group. These results indicates that the NDD-B type had lower levels of cognitive control, cognitive flexibility, and inhibitory control. For comparisons of each NDD subtype with the TD group, excluding the abovementioned cognitive performance aspect, see\u0026nbsp;Online Resource 3\u003cspan lang=\"EN-US\"\u003e.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWe also investigated the correlation between MM levels and cognitive performance (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 2b\u003c/span\u003e\u003c/strong\u003e). MM levels and execution time on the TMT-B were positively correlated in the NDD-B type (MHPG: \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.016; 5-HIAA: \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.019; HVA: \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-\u003cspan lang=\"EN-US\"\u003eFDR = 0.016). However, the TD group did not show this correlation. These results indicate that in the NDD-B type, enhancement of monoaminergic activity is linked to slower cognitive control.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eBrain structural characteristics\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBrain structural data are shown in \u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 3\u003c/span\u003e\u003c/strong\u003e. The main effect of group in the linear mixed-effects model showed that the NDD-A type exhibited a larger surface area in the right isthmus cingulate gyrus (\u003cem\u003e\u0026beta;\u003c/em\u003e = 1.02, 95% CI [0.49, 1.56], \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.29, \u003cem\u003et\u003c/em\u003e = 3.74, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FDR = 0.022, \u003cem\u003e\u003cspan lang=\"EN-US\"\u003ep\u003c/span\u003e\u003c/em\u003e-FWE = 0.044) than the TD group. However, the NDD-B type did not show significant differences in regional cortical surface areas. Additionally, whole-brain voxel-based GMV analysis using TFCE revealed a significantly larger cluster extending from the left middle frontal to the right middle frontal gyrus when comparing the NDD-B type with the TD groups (Online Resource 4). The primary peak within this cluster indicated smaller volumes in the left posterior orbital gyrus in the NDD-B\u0026nbsp;type\u0026nbsp;than in the TD group. Additional peaks revealed reduced volumes in the left posterior cingulate gyrus, left cuneus, left precuneus, left middle frontal gyrus, left superior occipital gyrus, right superior frontal gyrus, right supplementary motor cortex, and right posterior insula in the NDD-B\u0026nbsp;type\u0026nbsp;than in the TD group. In contrast, the NDD-A type did not exhibit significant differences in regional brain volumes. These results highlight the distinct patterns of structural brain differences between the NDD subtypes. Subsequently, we investigated the associations between MM levels and brain structural characteristics (where each NDD subtype showed greater surface or lesser GMV than did the TD group) (\u003cstrong\u003eFig\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e.\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e 3b\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e); no correlations were found between MM levels and brain structural characteristics.\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify urinary MM-based NDD subtypes across ADHD and ASD by using an unsupervised clustering approach and sought to examine structural brain characteristics associated with these subtypes. Cognitive performance was also examined to inform the interpretation of the identified subtypes. Clustering revealed two distinct NDD subtypes: 1) NDD-A, characterized by high MHPG, 5-HIAA, and HVA levels in urine; and 2) NDD-B, characterized by low levels of these three MMs. Although no significant differences in behavioral symptoms were observed between the two NDD subtypes, each of these demonstrated greater difficulties in social communication and sensory processing, severity of inattention and hyperactivity/impulsivity, and fatigue than did the TD group (\u003cb\u003eOnline Resource 2\u003c/b\u003e). Greater difficulty in social communication was associated with higher levels of MHPG in the NDD-A subtype. Moreover, compared to the TD group, only the NDD-B type showed reduced performance in cognitive control, cognitive flexibility, and inhibitory control. Notably, compared to the TD group, the cortical surface areas were larger in the isthmus cingulate gyrus in the NDD-A type, whereas the NDD-B type exhibited smaller GMVs in the posterior orbital gyrus, posterior cingulate gyrus, superior frontal gyrus, cuneus, precuneus, supplementary motor cortex, middle frontal gyrus, superior occipital gyrus, and posterior insula. These findings suggest that the NDD-A and NDD-B subtypes, which exhibit distinct monoamine activity patterns, show distinct patterns of structural brain differences.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMonoamine hyper- or hypofunction-subtype specificity\u003c/h2\u003e \u003cp\u003eWe found that the urinary levels of MHPG, 5-HIAA, and HVA were higher in the NDD-A than in the TD group, supporting the hypothesis of monoaminergic hyperactivity in NDDs (Chugani et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Dvor\u0026aacute;kov\u0026aacute; et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Goldberg et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ulke et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, the NDD-A type displayed a positive correlation between urinary MHPG levels and social communication difficulties, while no significant differences in cognitive function were found between the NDD-A and TD groups. Here, a relevant concept is the inverted U-shaped relationship between monoamine levels and cognitive function (Arnsten \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Baldi and Bucherelli \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), whereby high noradrenergic activity is associated with poorer social communication, but is better for maintaining cognitive performance.\u003c/p\u003e \u003cp\u003eThe noradrenergic pathway projects from the locus coeruleus to the amygdala and cingulate cortex (Heilbronner and Haber \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A pharmacological study reported that, after intraperitoneal injection of \u003cem\u003eN\u003c/em\u003e-(2-chloroethyl)-\u003cem\u003eN\u003c/em\u003e-ethyl-2-bromobenzylamine (a selective noradrenergic neurotoxin) in mice, the reduction in noradrenergic activity of the locus coeruleus increased anxiety-related social behavior and decreased noradrenaline content in the amygdala (Itoi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This indicated a role for the locus coeruleus\u0026ndash;amygdala noradrenergic circuit in social behavioral function. Moreover, Monk et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) revealed stronger connectivity between the amygdala and ventral anterior cingulate cortex in individuals with ASD than in TD controls when they viewed faces depicting socially relevant emotions. This suggests the involvement of atypical social information processing and heightened social sensitivity in this connectivity. Accordingly, noradrenergic hyperactivity in the locus coeruleus\u0026ndash;cingulate cortex\u0026ndash;amygdala pathway may contribute to social functioning difficulties in NDD-A, specifically given the association of increased urinary MHPG levels with social communication difficulties in NDD-A. Alternatively, increased serotonin levels, together with noradrenergic hyperactivity, may be related to social communication difficulties, because noradrenaline facilitates serotonin efflux by binding to the α1 receptor on serotonergic neurons (Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies have reported an association between elevated serotonin levels and social communication difficulties in ASD (Kheirouri et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our findings suggested that the interaction between the noradrenergic and serotoninergic systems may contribute to social communication impairment in NDD-A. We argue that individuals with NDD-A may be characterized as having hyperactivity of the noradrenergic rather than serotonergic system, with the former being specifically associated with social communication difficulties.\u003c/p\u003e \u003cp\u003eThe NDD-B subtype displayed lower levels of MHPG, 5-HIAA, and HVA in urine than did the TD group, supporting the hypothesis of monoaminergic hypoactivity in NDDs (Alabdali et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gevi et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gevi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hannestad et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hellings \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mechler et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ota et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This group also demonstrated poorer cognitive control, cognitive flexibility, and inhibitory control than that in the TD group. These findings suggested that the NDD-B subtype may exhibit executive function difficulties associated with monoaminergic hypoactivity. Moreover, higher urinary MM levels were associated with reduced cognitive control in the NDD-B subtype. Since the TMT-B has also been found to be associated with frontal executive function (Chan et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), its results must be combined with those of other executive function measures to assess cognitive control.\u003c/p\u003e \u003cp\u003eThe serotonergic system projects from the raphe nuclei to the prefrontal cortex via the ventral tegmental area and locus coeruleus (Hensler \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the prefrontal cortex receives direct projections from dopaminergic neurons in the ventral tegmental area and noradrenergic neurons in the locus coeruleus (Arnsten et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Aston-Jones and Cohen \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, binding of serotonin to serotonin-2A receptors on noradrenergic neurons in the locus coeruleus and dopaminergic neurons in the ventral tegmental area regulates the release of noradrenaline and dopamine into the prefrontal cortex (Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), highlighting the interaction between these monoamines in the prefrontal cortex. Through this modulation, monoaminergic activity may be related to executive function. Neuroimaging and pharmacological studies have reported that serotoninergic, noradrenergic, and dopaminergic activity modulates executive function, such as cognitive control and set shifting (Arnsten and Li \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Arnsten and Pliszka \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kuwamizu et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yamashita and Yamamoto \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that the interaction between these monoamines and their reduced availability in the prefrontal cortex may contribute to executive function difficulties in individuals with NDD-B. Unlike those with NDD-A, individuals with NDD-B may be particularly characterized as having a monoaminergic hypoactivity disorder specifically associated with executive function impairments. These aspects of monoaminergic activity heterogeneity highlight the need for using different approaches and considerations when diagnosing and treating NDDs across the identified subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDistinct brain structural anomalies in monoamine hyper- or hypofunction subtypes\u003c/h2\u003e \u003cp\u003eStructural analyses revealed significantly larger surface areas in the right isthmus cingulate gyrus in the NDD-A subtype group than in the TD group. In contrast, the NDD-B subtype displayed significantly smaller GMVs in the left posterior orbital gyrus, left posterior cingulate gyrus, right superior frontal gyrus, left cuneus, left precuneus, right supplementary motor cortex, left middle frontal gyrus, left superior occipital gyrus, and right posterior insula than those in the TD group. Thus, monoaminergic subtype specificity in NDD-A and NDD-B is associated with distinct brain structural differences.\u003c/p\u003e \u003cp\u003eSurface area enlargement in the NDD-A subtype was observed in the right isthmus cingulate gyrus. Anatomically, the isthmus cingulate is a key structure that connects the posterior cingulate cortex to the parahippocampal gyrus (Caeyenberghs et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although not well understood, the isthmus cingulate gyrus may be involved in emotional regulation and episodic memory processing, based on its association with anatomical connections (Weerasekera et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Interestingly, a greater surface area in the isthmus cingulate gyrus has been associated with worse social ability scores in individuals with ASD (Doyle-Thomas et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), suggesting the involvement of atypical isthmus cingulate gyrus morphometry in social behavioral impairments in NDDs. Studies on ASD have shown associations between an increase in cortical surface area and cerebral cortex overgrowth (Donovan and Basson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ohta et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), suggesting that an increased cortical surface area, possibly linked to abnormal gyrification (Kitajima et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), may lead to neuronal overgeneration and abnormal neural connections (Hogstrom et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Meyer et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the cingulate cortex, including the isthmus cingulate gyrus, is a key target of the ascending noradrenergic pathway originating from the locus coeruleus (Heilbronner and Haber \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Joshi et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that the noradrenergic activity in the locus coeruleus\u0026ndash;cingulate cortex circuit, together with the amygdala, may affect social functional impairments through its modulation of emotional regulation (Itoi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Monk et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Terbeck et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our findings suggest that surface area enlargement in the isthmus cingulate gyrus may be a key factor in the social communication difficulties observed in individuals with the NDD-A subtype, potentially due to alterations in noradrenergic neural connections.\u003c/p\u003e \u003cp\u003eWe demonstrated GMV reductions in the posterior orbital gyrus, posterior cingulate gyrus, superior frontal gyrus, precuneus, supplementary motor cortex, and middle frontal gyrus of the NDD-B subtype group as compared to the TD group. The superior frontal gyrus, middle frontal gyrus, and supplementary motor cortex correspond to executive functions (Deng et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Duan et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sj\u0026ouml;berg et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although not fully understood, the posterior orbital gyrus is located within the lateral orbitofrontal cortex and may play a role in executive function processes, together with the frontal areas (Deng et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rolls \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Functional connections in the orbitofrontal area, such that cognitive control and inhibitory control are represented predominantly in the frontoparietal network, have been proposed previously (L\u0026uuml;ckmann et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tozzi et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As differences in the structural and functional connectivity of the orbitofrontal area have been implicated in ADHD (Li et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e), these frontal areas and their association with these networks may underlie the functional architecture of executive function differences in NDDs. Furthermore, the precuneus and posterior cingulate cortex comprise the functional core of the default mode network (Utevsky et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), indicating that suppression of this network may be associated with a reduction in cognitive control and inhibitory control (Ishihara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). GMV reductions in these frontal\u0026ndash;precuneus\u0026ndash;posterior cingulate areas observed in NDD-B may be associated with executive function difficulties through differences in structural and functional connectivity, such as in the frontoparietal and default mode networks. At the molecular level, a previous study reported that Nagase analbuminemic rats, an animal model of NDD, showed lower serotonin levels in the prefrontal cortex (Hakamada and Yamamoto \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), suggesting the importance of serotonin synthesis in NDDs. Moreover, serotonin facilitates noradrenaline and dopamine efflux via serotonin-2A receptor activity in the frontal cortex (Bortolozzi et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Stahl \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, van der Meer et al. (2015) reported that S-allele carriers with abnormal serotonin transporter expression have smaller GMVs in the frontal lobe, noting a correlation between frontal GMV and ADHD symptoms, suggesting the involvement of serotonin dysregulation in ADHD severity. These aspects of excessive serotonin may contribute to structural changes in the frontal\u0026ndash;precuneus\u0026ndash;posterior cingulate loci in NDD-B via noradrenaline and dopamine enhancement. Consequently, GMV reductions in these regions may lead to difficulties in executive function in individuals with NDD-B.\u003c/p\u003e \u003cp\u003eOur results also showed that the NDD-B type had smaller volumes in the cuneus, superior occipital gyrus, and posterior insula than the TD group. The cuneus and superior occipital gyrus are important for visual processing and spatial attention (Astafiev et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Atypical functional connectivity between the superior occipital gyrus and motor cortex may contribute to visual-motor dysfunction in children with ASD (Nebel et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A meta-analytic connectivity modeling study found coactivation of the posterior insula in sensorimotor and interoceptive processing, and language-related functions (Chang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although further investigation is needed to clarify the functional implications, the GMV reductions in these regions may suggest that individuals with NDD-B could exhibit visual-attention, sensory-integration, and language-processing difficulties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications and limitations\u003c/h2\u003e \u003cp\u003eThe National Institute of Mental Health in the United States launched the Research Domain Criteria projects to establish a novel framework for pathophysiological research (Insel and Cuthbert \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, the present study aimed to study urinary MM patterns categorized by unsupervised machine learning and brain structural characteristics in individuals with ADHD and/or ASD. Despite clear differences in MMs, behavioral symptoms did not differ between NDD subtype groups; these could possibly be caused by other molecular and neural mechanisms. Additionally, our findings highlight the importance of personalized therapeutic strategies and reconsideration of pharmacological intervention. As NDD-A is characterized by monoaminergic hyperactivity, methylphenidate, atomoxetine, and serotonin reuptake inhibitors may not be suitable for such patients. Alternatively, a treatment option is branched-chain amino acids (BCAAs), given their association with regulation of serotonin and its precursor tryptophan (Yamashita \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As BCAAs share the system L transporter located on the surface of the blood\u0026ndash;brain barrier with tryptophan, they may reduce excessive serotonin synthesis by regulating tryptophan availability in the brain (Yamashita \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because BCAAs are naturally occurring amino acids found in food and supplements, they may offer a safe and non-pharmacological alternative for managing monoaminergic dysregulation in NDD-A.\u003c/p\u003e \u003cp\u003eOur study had some limitations. First, our clustering results might be considered preliminary due to the small sample size for NDD-A, which limited the statistical power and generalizability of the results of the study. Future studies with large cohorts are warranted to validate our findings. Second, our study was cross-sectional; thus, future longitudinal studies are needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we reclassified children with ADHD and ASD into NDD categories based on new phenotypes using noninvasive urinary MM levels and analyzed their neurobiological characteristics after considering various confounding factors. Such an approach has not been described in this field previously. Our results demonstrated that monoaminergic hyper- or hypofunctional specificity in individuals with NDD-A and NDD-B, respectively, distinct brain structural anomalies. Our study presents a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps in ADHD and ASD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eCompliance with Ethical Standards\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan lang=\"EN-US\"\u003eConflict of Interest\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan lang=\"EN-US\"\u003eEthics Approval\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe Research Ethics Committee of the University of Fukui approved the study protocol (Assurance No. FU-20220061). The procedures complied with the principles of the Declaration of Helsinki.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan lang=\"EN-US\"\u003eConsent to Participate\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eAll parents provided written informed consent and all children provided assent for participation.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eData Availability\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe data will be made available via the Child Developmental MRI (CDM) Project database, which is currently under construction. Additionally, data will be provided upon signing a data sharing agreement and after receiving a brief research proposal along with evidence of approval from the requester\u0026rsquo;s institutional review board.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eFunding\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (grant numbers: 23K12814 to Masatoshi Yamashita), a grant from Taiju Life Social Welfare Foundation (award year 2023 and 2025 to Masatoshi Yamashita), Life Science Innovation Center (grant number: LSI24101 to Masatoshi Yamashita), the Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (award year 2022 to Yoshifumi Mizuno), and Research Grants from the University of Fukui (academic years 2022, 2023, and 2024 to Yoshifumi Mizuno). These funding sources were not involved in the study design or implementation; the collection, analysis, or interpretation of data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eAlabdali A, Al-Ayadhi L, El-Ansary A (2014) Association of social and cognitive impairment and biomarkers in autism spectrum disorders. J Neuroinflammation 11: 4.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eAmin F, Davidson M, Davis KL (1992) Homovanillic acid measurement in clinical research: a review of methodology. Schizophr Bull 18: 123-48.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eAntshel KM, Russo N (2019) Autism Spectrum Disorders and ADHD: Overlapping Phenomenology, Diagnostic Issues, and Treatment Considerations. 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Neuroreport 29: 1188-1192.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eYang Z, Li H, Tu W, Wang S, Ren Y, Yi Y, Wu T, Jiang K, Shen H, Wu J, Dong X (2018b) Altered patterns of resting-state functional connectivity between the caudate and other brain regions in medication-na\u0026iuml;ve children with attention deficit hyperactivity disorder. Clin Imaging 47: 47-51.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eYu W, Wang Q, Ge M, Shi X (2022) Cluster analysis of lymphocyte subset from peripheral blood in newly diagnosed idiopathic aplastic anaemia patients. Ann Med 54: 2431-2439.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eZhang M, Huang Y, Jiao J, Yuan D, Hu X, Yang P, Zhang R, Wen L, Situ M, Cai J, Sun X, Guo K, Huang X (2022) Transdiagnostic symptom subtypes across autism spectrum disorders and attention deficit hyperactivity disorder: validated by measures of neurocognition and structural connectivity. BMC Psychiatry 22: 102.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan lang=\"EN-US\"\u003eZhao Y, Cui D, Lu W, Li H, Zhang H, Qiu J (2020) Aberrant gray matter volumes and functional connectivity in adolescent patients with ADHD. J Magn Reson Imaging 51: 719-726.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1\u003cspan lang=\"EN-US\"\u003e. Demographics of the typical development and neurodevelopmental disorder groups\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd height=\"79\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eCharacteristic\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"79\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eTD\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(n = 83)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"79\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNDD\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e(n = 83)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"79\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cspan lang=\"EN-US\"\u003eP\u003c/span\u003e\u003c/em\u003e\u003cspan lang=\"EN-US\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"68\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eAge (years)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10.76 (2.97)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e11.75 (3.13)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"68\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eEducation (years)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e6.19 (2.96)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e7.27 (3.06)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"96\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eSex (n)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMale\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eFemale\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"96\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e39\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e44\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"96\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e74\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"96\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026lt; 0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"40\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eAnnual household income (JPY) (n)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"40\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"40\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"40\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"125\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026lt; 3 000 000\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3 000 000\u0026ndash;5 000 000\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e5 000 000\u0026ndash;7 000 000\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026ge; 7 000 000\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e20\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e48\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e24\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e17\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.002\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"125\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDiagnosis (n)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eADHD\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eASD\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eADHD+ASD\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e21\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e41\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"125\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eHandedness (n)\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRight\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eLeft\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eAmbidextrous\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e78\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e75\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"125\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e0.147\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"68\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIntelligence quotient (score)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e106.28 (12.77)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e96.67 (12.75)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"68\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u0026lt; 0.001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eParameters are indicated as the mean (\u003cem\u003eSD\u003c/em\u003e) or n. \u003cem\u003eP\u003c/em\u003e-values for age, education, income, and intelligence quotient are based on \u003cem\u003et\u003c/em\u003e-tests for the comparison of the TD group with each NDD subtype. \u003cem\u003eP\u003c/em\u003e-values for sex ratio and handedness ratio are based on C\u003cspan lang=\"EN-US\"\u003ehi-square tests for the comparison of TD with each NDD subtype.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eADHD, attention-deficit/hyperactivity disorder; ASD, autism spectrum disorder; NA, not applicable; NDD, neurodevelopmental disorder; \u003cem\u003eSD\u003c/em\u003e, standard deviation; TD, typical development.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"brain structure, cross-disorder phenotypic overlap, monoamine metabolite, neurodevelopmental disorder, unsupervised machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8477091/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8477091/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeuroimaging and molecular studies have attempted to elucidate the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, their findings have been inconsistent because of within-disorder heterogeneity and cross-disorder phenotypic overlap. We sought to identify monoamine-based subtypes across ADHD and ASD and to clarify their distinct brain structural characteristics. In 83 children with ADHD and/or ASD, we applied unsupervised machine learning (NbClust with K-means) to identify novel neurodevelopmental disorder (NDD) phenotypes using urinary monoamine metabolite profiles. Behavioral symptoms, cognitive performance, cortical surface area, and gray matter volume (GMV) were then evaluated for each NDD phenotype and for 83 children with typical development (TD). Clustering identified two NDD phenotypes: NDD-A (n\u0026thinsp;=\u0026thinsp;18, characterized by high levels of 4-hydroxy-3-methoxyphenylglycol, 5-hydroxyindoleacetic acid, and homovanillic acid) and NDD-B (n\u0026thinsp;=\u0026thinsp;65, characterized by low levels of these monoamine metabolites). Moreover, urinary 4-hydroxy-3-methoxyphenylglycol levels correlated positively with social communication difficulties in NDD-A. Behaviorally, the NDD-B group showed significantly lower levels of cognitive control, cognitive flexibility, and inhibitory control than did the TD group. Structurally, compared to the TD group, the NDD-A group showed significant surface area enlargement in the isthmus cingulate gyrus, whereas the NDD-B group exhibited significant GMV reductions in the frontal lobe, precuneus, posterior cingulate cortex, and superior occipital area. These findings suggest that monoaminergic hyper- or hypofunction in individuals with NDD-A and NDD-B is associated with distinct brain structural differences. Such phenotype specificity may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps.\u003c/p\u003e","manuscriptTitle":"Transdiagnostic monoamine-based subtyping for attention- deficit/hyperactivity disorder and autism spectrum disorder via unsupervised machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:11:30","doi":"10.21203/rs.3.rs-8477091/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-14T09:45:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T11:05:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183574918013282413807936019868502549582","date":"2026-03-26T10:57:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T10:32:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-04T10:07:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T23:01:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neural Transmission","date":"2025-12-30T03:40:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"journal-of-neural-transmission","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Journal of Neural Transmission","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3535ead2-81f2-4957-b728-c60c746ba9f9","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T09:55:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:11:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8477091","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8477091","identity":"rs-8477091","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}