Population neuroscience of ADHD: Growth charts uncover neurodiversity
Poster No:
322
Submission Type:
Abstract Submission
Authors:
Ningning Liu1, Yinshan Wang2, Zixuan Zhou2, Peng Gao2, Xinyi Zhang1, Ziqing Zhu1, Yuan Gao1, Li-Zhen Chen2, Haimei Li1, Changxi Ju1, Yufeng Wang1, Lu Liu1, Qiujin Qian1, Xi-Nian Zuo2, Lifespan Brain Chart Consortium2
Institutions:
1Peking University Sixth Hospital, Beijing, Beijing, 2State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing
First Author:
Co-Author(s):
Yinshan Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Zixuan Zhou
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Peng Gao
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Li-Zhen Chen
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Xi-Nian Zuo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Lifespan Brain Chart Consortium
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Introduction:
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder (Shaw et al., 2010). According to the DSM-5, ADHD symptoms must be inconsistent with the individual's developmental level (APA, 2013). Currently, this inconsistency is assessed subjectively by physicians rather than through objective, developmentally aligned criteria, largely due to a limited understanding of the neurodevelopmental mechanisms underlying ADHD. The differences between ADHD developmental levels and typical development remain unclear. Recent neuroimaging advancements have sought to clarify these traits, indicating that ADHD reflects a delay in brain development (Shaw et al., 2018; Shaw et al., 2007; Shaw et al., 2012; Hoogman et al., 2017; Hoogman et al., 2019). However, this delay has a small effect size and is insufficient for clinical applications, partly due to ADHD's high heterogeneity. Recent studies have aimed to classify ADHD subtypes based on pathophysiological mechanisms to improve clinical translation (Bu et al., 2024; Costa et al., 2015; Feng et al., 2024; Lecei et al., 2019). While these studies support distinct neurodevelopmental subtypes, the absence of reliable, globally-consistent metrics for normative neurodevelopmental deviations limits broader applicability, complicating the identification of relevant mechanisms and the explanation of varied developmental characteristics. This study uses normative brain charts from 123,984 structural MRI scans (Bethlehem et al., 2022) and extensive multimodal data, including genetic, brain, behavioral, and environmental measurements, to explore ADHD neurodiversity.
Methods:
After quality control and processing, we calculated centile scores for brain morphology in 270 children with ADHD (ages 6-17), all diagnosed by two psychiatrists. We identified deviations from the normative model and clustered ADHD individuals using partitioning around medoids to uncover trait constellations underlying ADHD's clinical heterogeneity. We compared multidimensional data, including clinical symptoms, neurocognitive outcomes, brain function, and genetic and environmental risk factors, to dissect this heterogeneity and support tailored early diagnosis or therapeutic approaches from a population neuroscience perspective (Figure 1).
Results:
Analysis revealed widespread regional deviations in cortical volume among children with ADHD, highlighting significant individual differences. Further investigation identified two subgroups with distinct brain development patterns: delayed brain growth (DBG-ADHD) and precocious brain growth (PBG-ADHD). The DBG-ADHD subgroup exhibited notable neurocognitive impairments and higher functional homotopy within the default-mode network, frequently implicated in ADHD research. Differentially expressed genes in this group were related to neurodevelopment, with various prenatal risk factors affecting brain development. In contrast, PBG-ADHD was linked to increased disruptive behaviors, higher functional homotopy in language and somatomotor networks, and neurogenesis-related genetic pathways, with no significant prenatal environmental risk factors noted (Figure 2).
Conclusions:
Children with ADHD exhibit considerable individual variation in brain development, characterized by two distinct subgroups with different developmental traits despite similar clinical symptoms. One subgroup shows comprehensive developmental delays likely due to genetic and prenatal environmental factors, while the other presents congenital brain abnormalities possibly from atypical brain morphogenesis or adverse environmental influences, often associated with disruptive behaviors. These findings provide crucial insights into ADHD's neurodevelopmental diversity.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Keywords:
Attention Deficit Disorder
Other - growth chart, genetics; environment; interhemispheric communication
1|2Indicates the priority used for review
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Provide references using APA citation style.
American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC (2013): American psychiatric association.
Bethlehem, R. (2022). Brain charts for the human lifespan [Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't]. Nature, 604(7906), 525-533.
Bu, X. (2024). Normative growth modeling of brain morphology reveals neuroanatomical heterogeneity and biological subtypes in children with ADHD. bioRxiv, 2023-2024.
Costa, D. T. (2015). Characterizing heterogeneity in children with and without ADHD based on reward system connectivity [Journal Article; Research Support, N.I.H., Extramural]. Developmental Cognitive Neuroscience, 11, 155-174.
Feng, A. (2024). Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance. Eclinicalmedicine
Lecei, A. (2019). Can we use neuroimaging data to differentiate between subgroups of children with ADHD symptoms: A proof of concept study using latent class analysis of brain activity [Journal Article; Research Support, Non-U.S. Gov't]. Neuroimage-Clinical, 21, 101601.
Shaw, P. (2010). Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories [Journal Article; Review]. Human Brain Mapping, 31(6), 917-925.