Jul 2026· Journal of neural transmission· Vol 133, pp. 1937 - 1952· 0 citations· 98 references
Medicine
TL;DR
It is suggested that urinary MM-defined NDD-A and NDD-B phenotypes are associated with distinct cognitive and brain structural characteristics and may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps.
Abstract
Neuroimaging and molecular studies have examined the etiology of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). However, their findings remain inconsistent because of within-disorder heterogeneity and cross-disorder phenotypic overlap. We sought to identify monoamine-based subtypes across ADHD and ASD and clarify their brain structural characteristics. In 83 children with ADHD and/or ASD, we applied unsupervised machine learning (NbClust with K-means) to identify neurodevelopmental disorder (NDD) phenotypes using urinary monoamine metabolite (MM) profiles. Behavioral symptoms, cognitive performance, cortical surface area, and gray matter volume (GMV) were evaluated for each NDD phenotype and for 83 typically developing (TD) children as controls. Clustering identified two urinary MM-defined NDD phenotypes: NDD-A (n = 18, including 5 ADHD, 2 ASD, and 11 ADHD + ASD cases), characterized by high levels of 4-hydroxy-3-methoxyphenylglycol, 5-hydroxyindoleacetic acid, and homovanillic acid, and NDD-B (n = 65, including 16 ADHD, 19 ASD, and 30 ADHD + ASD cases), characterized by low levels of these metabolites. Urinary 4-hydroxy-3-methoxyphenylglycol levels correlated positively with social communication difficulties in NDD-A. NDD-B showed significantly lower cognitive control, cognitive flexibility, and inhibitory control than TD. Structurally, compared with TD, NDD-A showed significant surface area enlargement in the isthmus cingulate gyrus, whereas NDD-B exhibited significant GMV reductions primarily in fronto–opercular/orbitofrontal regions, with additional reductions in the superior parietal lobule and supramarginal gyrus. These findings suggest that urinary MM-defined NDD-A and NDD-B phenotypes are associated with distinct cognitive and brain structural characteristics. Such phenotype specificity may provide a novel framework for understanding within-disorder heterogeneity and cross-disorder phenotypic overlaps.
Using a portable EEG device with a low participant burden and a deep learning model to distinguish between the typical development group (TD) and the NDD group, comprising children with ASD, ADHD, and ASD + ADHD is used.
Individuals with prenatal alcohol exposure (PAE) and fetal alcohol spectrum disorders (FASDs) present a range of neurodevelopmental deficits (e.g., inattention, hyperactivity, and executive dysfunction) which have shown marked overlap with attention‐deficit/hyperactivity disorder (ADHD), making differential diagnosis challenging. While rates of comorbidity are high, evidence has suggested there are important distinctions between neurodevelopmental phenotypes. To understand these distinctions, we evaluated whether proposed neurobehavioral disorder associated with prenatal alcohol exposure (ND‐PAE) criteria in the appendix of the Diagnostic and Statistical Manual for Mental Disorders (Fifth Edition) can differentiate FASD from ADHD. We conducted systematic searches across three databases (Medline, PsycINFO, PubMed) to identify studies comparing behavioral and cognitive functioning between FASD, ADHD, and healthy controls (HCs). Outcomes indicated FASD individuals have greater magnitude neurodevelopmental deficits than ADHD; however, no differences were found regarding the pattern of deficit because of methodological limitations, such as low I2. Moreover, outcomes supported adaptive and neurocognitive (executive functioning) criteria but did not support self‐regulation criteria. These findings collectively underscore a need for clinicians to consider the magnitude of deficits presented to facilitate accurate recognition of PAE. Further research characterizing ND‐PAE criteria and differences in the magnitude of deficits between disorders may ultimately support more accurate differential diagnosis.
Todd J Capes, Silvio Aldrovandi, Reshmail Khan et al.· Annals of the New York Acade...· 0 citations
A high frequency of positive screening results for symptoms suggestive of ADHD and ASD, significant overlap between these conditions, and a low proportion of self-reported formal diagnoses are revealed.
Fernanda Santos Marcuz Cavalaro, Josieli de França Oliveira Lima, Gabriela Costa Alves· Revista de Estudos Interdisc...· 0 citations
Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are conventionally thought to be very distinct neurodevelopmental disorders, but many autistic individuals also exhibit symptoms of ADHD. Extensive research has focused on examining the behavioral profiles of autistic individuals with comorbid ADHD, but their neurobiological profiles remain understudied. The current study aimed to examine the intrinsic connectivity of the hippocampal memory, default-mode, central executive, and salience networks in autistic individuals with or without ADHD. Using data from the Autism Brain Imaging Data Exchange (ABIDE I), we identified 23 individuals with autistic disorder and comorbid ADHD (Comorbid), 23 individuals with autistic disorder only (ASD-only), and 23 non-autistic individuals (non-ASD), who were well-matched on gender, age, and full-scale IQ. Results showed that the Comorbid group showed hypo-connectivity of the hippocampal memory, DMN, CEN, and SN nodes compared to the ASD-only group. In addition, the connectivity of the right hippocampus and PCC showed distinct relationships with the ASD symptom severity in autistic individuals with and without ADHD. These findings indicated that the comorbid condition has a distinct neurobiological profile compared to the ASD-only group, suggesting that future intervention and education should have specialized programs to support affected individuals with a focus on their memory functions in addition to their socio-communicative challenges.
Unknown authors· Frontiers in Psychiatry· 0 citations
Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder and is a risk factor for later brain disorders. Methods: Here, we characterize the relationship between ADHD status and white matter cellularity across development and examine associations with medication, using a novel biophysical diffusion MRI model in the ABCD Study® cohort (N=10,526; baseline MRI: 9.92±0.63 years, 12.2% with ADHD; MRI Wave 2: 11.95±0.65 years; 11.3% with ADHD, MRI Wave 3: 14.07±0.69 years, 11.8% with ADHD). Twenty-seven white matter tracts were delineated using multi-shell diffusion-weighted imaging and tractography, with intracellular isotropic (RNI) and directional (RND) diffusion quantified using the Restriction Spectrum Imaging (RSI) model. Longitudinal linear mixed-effect models characterize the effects of ADHD status and medication use on white matter RNI and RND across three biennial MRI waves. Results: ADHD was associated with decreased RNI in 20 tracts at age 9, with evidence of developmental trajectory differences suggesting attenuation over early adolescence. Enduring ADHD-associated decreases in RND were observed spanning ages 9 to 14 years in 16 tracts, with methylphenidate effects on 2 tracts. RNI but not RND findings were robust in low-motion sensitivity analyses. Exploratory analyses of ADHD severity suggest attenuation of RNI differences were paralleled by reductions in ADHD symptoms. Conclusions: Altogether, ADHD was robustly associated with reductions in isotropic diffusion in white matter tracts, suggestive of atypical glial cellularity during late childhood. Complementary reductions in directional diffusion of select tracts may suggest atypical axonal organization enduring across early adolescence.
L. N. Overholtzer, Katherine L. Bottenhorn, H. Ahmadi et al.· Biological Psychiatry: Cogni...· 0 citations
Background/Objectives: Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are frequently co-occurring neurodevelopmental conditions with partially overlapping neurophysiological profiles. Electroencephalography (EEG) provides non-invasive access to candidate biomarkers, yet the literature remains largely organized around single-diagnosis frameworks, limiting comparison across conditions and constraining translation into intervention selection. This review compares EEG signatures across ASD and ADHD from a transdiagnostic perspective and examines how such signatures might inform the selection of non-pharmacological interventions. Methods: A structured search of PubMed, Scopus, IEEE Xplore and Web of Science identified peer-reviewed studies published between 2010 and 2026 reporting EEG findings in ASD and/or ADHD, spanning resting-state, task-based, connectivity, event-related potential, machine learning and intervention studies. Sixty-eight sources were synthesized thematically. Given substantial heterogeneity in acquisition parameters and analytic pipelines, evidence was integrated interpretively rather than pooled quantitatively, and no formal risk-of-bias assessment was undertaken. Results: Shared features across both conditions frequently included low-frequency theta excess, reduced alpha modulation under cognitive load, and flattened aperiodic (1/f) slopes—a pattern compatible with, though not a direct measurement of, altered excitation/inhibition balance. While substantial heterogeneity exists, disorder-specific signatures often comprised the ASD “U-shaped” spectral profile alongside elevated epileptiform activity, and frontally pronounced theta/beta ratio elevation in subsets of individuals with ADHD. Machine-learning studies increasingly emphasize interpretable, multidomain feature sets over binary classification. Mindfulness-based and neurofeedback interventions converge on theta reduction and alpha enhancement, although reported effects are frequently conditional on responder status, task context, or outcome-rater blinding. Conclusions: Convergent EEG features support a transdiagnostic account of neurodevelopmental dysregulation. A biomarker-informed framework for intervention selection is proposed, which requires prospective validation before clinical application.
Akshay Bhuvaneswari Ramakrishnan, N. NavaneethaKrishnan, William Mahler et al.· Brain Science· 0 citations
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