A groundbreaking new study published in the renowned journal Molecular Psychiatry is reshaping our understanding of the nuanced neurobiological relationship between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). This research, spearheaded by Dr. Adriana Di Martino and her team at the Child Mind Institute, challenges the long-standing categorical boundaries between these two prevalent neurodevelopmental conditions. The study’s central revelation is that the severity of autism symptoms, rather than strict diagnostic labels, aligns with distinct brain connectivity patterns and corresponding gene expression profiles across children diagnosed with either ASD or ADHD.
The research employed advanced resting-state functional magnetic resonance imaging (rs-fMRI) techniques to analyze the intrinsic brain connectivity in a cohort of 166 verbally capable children aged between 6 and 12. These participants included children formally diagnosed with autism as well as those diagnosed with ADHD but without a formal autism diagnosis. The crux of the findings lies in the frontoparietal (FP) and default-mode (DM) brain networks—two widely studied neural circuits heavily implicated in executive functions and social cognition. Intriguingly, increased connectivity between these networks emerged as a consistent marker correlating with heightened autism symptom severity, independent of the child’s principal clinical diagnosis.
Typically, during childhood maturation, connectivity between FP and DM networks attenuates, facilitating specialized cognitive processing. However, the study reveals a disruption or atypical maturation in this connectivity pattern among children exhibiting more pronounced autism-related symptoms. This anomalous hyperconnectivity may underpin difficulties in social interaction and executive functioning that define autistic phenotypes. Moreover, this neural signature was observed not only in children with an ASD diagnosis but also in a subset of children diagnosed with ADHD, thereby underscoring the overlap in neurodevelopmental pathways driving these disorders.
Complementing the neuroimaging data, the investigators integrated a cutting-edge computational technique known as in silico spatial transcriptomic analysis. This approach enables the mapping of observed brain connectivity patterns onto existing gene expression databases. The confluence of neuroimaging findings and gene expression profiles revealed a significant association with genes integral to neural development, many of which have been independently implicated in both autism and ADHD. This dual genetic involvement points toward a shared molecular architecture influencing the emergence of overlapping symptoms in these neurodevelopmental disorders.
The implications of such shared biological substrates extend beyond academic curiosity; they hold tangible clinical significance. Traditional psychiatric diagnosis often segregates ASD and ADHD based on symptom clusters and behavioral criteria. However, Dr. Di Martino articulates that many children clinically diagnosed with ADHD manifest autism-like features that do not fully meet existing diagnostic thresholds for ASD. This study’s findings advocate for a dimensional rather than categorical diagnostic perspective, emphasizing symptom severity and the underlying neurobiology shared across disorders. This approach promises to inform more tailored and effective intervention strategies reflecting an individual’s unique neural and genetic profile.
The Child Mind Institute’s Healthy Brain Network (HBN), a pioneering initiative that provides comprehensive no-cost diagnostic evaluations and collects rich neuroimaging and phenotypic data from thousands of children, served as a critical resource underpinning this research. The availability of such extensive datasets coupled with sophisticated computational analytic tools represents a new frontier for neurodevelopmental psychiatry. It enables researchers to transcend traditional diagnostic stovepipes and explore the spectrum and dimensions of symptom presentations within and across disorders.
The study’s reliance on rs-fMRI is particularly noteworthy. Resting-state imaging captures spontaneous brain activity, offering insights into the brain’s functional architecture without task-specific demands. This imaging modality has gained traction for elucidating network-level brain dysconnectivity that could underlie behavioral phenotypes characteristic of ASD and ADHD. Moreover, the identified FP-DM hyperconnectivity serves as a candidate neural biomarker for autism symptom severity, holding promise for future translational applications in clinical diagnostics and monitoring.
Another key dimension to this research is the integration of neural developmental gene expression with functional network maturity. The frontal and parietal lobes implicated in the FP network, along with hubs of the DM network, undergo protracted maturation during childhood and adolescence. Disruptions in gene expression patterns related to neurogenesis, synaptic pruning, and myelination may consequently manifest as altered network connectivity. The convergence of connectivity and transcriptomic data thereby offers a more comprehensive biological framework for understanding the multifaceted etiology of autism and ADHD symptom overlap.
This study’s findings challenge existing psychiatric nosology by demonstrating that neurodevelopmental disorders may not be discrete entities but rather part of a dimensional continuum. The neurobiological commonalities between ASD and ADHD symptom severity suggest convergent developmental mechanisms influenced by shared genetic factors. Such insights are pivotal in reshaping diagnostic criteria that more accurately reflect brain-behavior relationships and guide precision medicine approaches.
Beyond diagnostic reevaluation, the research carries significant promise for biomarker discovery. The novel integrative methodology combining connectomics with spatial transcriptomics lays the groundwork for identifying reliable biological markers associated with symptom dimensions across neurodevelopmental disorders. These biomarkers could facilitate earlier diagnosis, track symptom progression, and ultimately tailor therapeutic interventions based on individual neural circuitry and genetic profiles.
The research team acknowledges that uncovering these shared neurobiological substrates is a transformative step toward transcending categorical diagnostic paradigms in psychiatry. The emphasis on dimensional, transdiagnostic, and data-driven models reflects a paradigm shift toward understanding mental health disorders as spectra rather than rigid categories. Such an approach harmonizes with ongoing efforts within the psychiatric research community to integrate genetics, neuroimaging, and behavioral data to unravel the complexity of neurodevelopmental disorders.
Future studies building on this work may expand the sample size, diversity, and age ranges to capture a dynamic developmental trajectory. Additionally, longitudinal analyses tracking changes in brain connectivity and gene expression over time could elucidate how these markers interact with environmental factors and clinical interventions. This trajectory-based insight may enable personalized medicine strategies that dynamically adjust to developmental changes in symptomatology.
Dr. Di Martino’s research is a seminal addition to the burgeoning field of neurodevelopmental neuroscience, providing empirical evidence to support clinically observed symptom overlaps between autism and ADHD. By pinpointing shared functional network alterations and genetic underpinnings, the study offers hope for a more integrated, biologically grounded understanding of these complex childhood disorders. This evolving framework has the potential to revolutionize treatment approaches and optimize outcomes for children worldwide.
As the Child Mind Institute continues its commitment to open science and collaborative research, studies like this illuminate the path toward more nuanced, precise, and effective neuropsychiatric care. The confluence of advanced neuroimaging, computational genomics, and dimensional clinical phenotyping heralds a new era where the boundaries of diagnosis dissolve in favor of individualized insight and treatment innovation.
Subject of Research: People
Article Title: Connectome-based symptom mapping and in silico related gene expression in children with autism and/or attention-deficit/hyperactivity disorder
News Publication Date: November 2025
Web References: 10.1038/s41380-025-03205-8
Keywords: Autism, Attention deficit hyperactivity disorder, Neuroscience, Developmental neuroscience, Mental health, Research on children, Functional magnetic resonance imaging, Clinical psychiatry
