Biotype Identification Based on Topological Deviations of Morphometric Similarity Network in ADHD
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M1 & M2 (Mezzanine Level)
Poster No:
265
Submission Type:
Abstract Submission
Authors:
Nanfang Pan1, Yajing Long2, Xiaoyong Lin3, Ying Chen2, Alex Fornito1, Qiyong Gong2
Institutions:
1Monash University, Clayton, Victoria, 2West China Hospital of Sichuan University, Chengdu, Sichuan, 3Zhengzhou University, Zhengzhou, Henan
First Author:
Co-Author(s):
Introduction:
Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by considerable clinical heterogeneity across inattentive and hyperactive/impulsive symptom domains. Understanding the heterogeneous neural mechanisms underlying ADHD could enhance opportunities for individualized management. To this end, normative modeling offers an effective method for identifying individual deviations from typical development. While previous normative modeling studies on ADHD primarily focused on regional morphological alterations, how these alterations are coupled between regions is unclear. This study investigates whether a hub-oriented fusion framework-which integrates multimodal topological deviations in morphometric similarity networks based on normative modeling-can provide robust neuromarkers for ADHD subtyping.
Methods:
We analyzed T1-weighted MRI data from six datasets comprising 446 ADHD children and 708 controls (discovery cohort, workflow in Figure 1), in addition to data from the Healthy Brain Network initiative, which includes 554 ADHD children and 123 controls (validation cohort). Based on gray matter volume distribution concordance assessed by Kullback-Leibler divergence similarity (Wang et al., 2016), we constructed morphometric similarity networks and investigated their hub organization through degree centrality (DC), nodal efficiency (NE), and participation coefficient (PC). We then developed normative models for these topological properties and assessed regional heterogeneity of extreme deviations in ADHD (Rutherford et al., 2022). Joint components of multimodal topological patterns were identified using multiset canonical correlation analysis (mCCA) plus joint independent component analysis (jICA) (Sui et al., 2018). To identify distinct biotypes and their associated clinical and neural profiles, we applied the Heterogeneity through Discriminative Analysis (HYDRA) algorithm (Varol et al., 2017). Molecular signatures of these biotypes were characterized through their relationships with neurotransmitter receptor distributions (Hansen et al., 2022). We further contextualized our findings in broader psychological processes incorporating Neurosynth-based task activation maps (Kent et al., 2024). Finally, we validated the generalizability of our ADHD biotype clustering solution.
·Figure 1. Schematic Overview of Analytical Procedures.
Results:
In the discovery cohort, ADHD was associated with distinct patterns of extreme deviation that spatially overlapped with controls. DC differences were prominent in the caudate and hippocampus, NE differences were distributed across the hippocampus and pallidum, and spatial overlap in PC emerged in the inferior frontal gyrus and orbitofrontal cortex. Fusion analysis yielded a group-discriminative component with covarying patterns predominantly localized to the orbitofrontal cortex. Three biotypes emerged, characterized by severe overall symptoms (Biotype 1), predominant hyperactive/impulsive features (Biotype 2), and marked inattentive symptoms (Biotype 3), with neural differences in the anterior cingulate cortex, pallidum, and superior frontal gyrus (Figure 2). Topological deviations in Biotype 1 showed significant positive spatial correspondence with serotonin and dopamine systems, while Biotypes 2 and 3 exhibited distinct negative spatial distributions. Biotype-specific cognitive terms aligned with their predominant clinical profiles. The validation cohort replicated the discovery findings, showing a progressive decrease in hyperactive/impulsive severity from Biotype 1 to 3.
·Figure 2. ADHD Biotype Identification Based on HYDRA Modeling.
Conclusions:
Our study advances the understanding of ADHD heterogeneity through a novel hub-oriented fusion framework that integrates multimodal topological deviations in morphometric similarity networks. By identifying three distinct biotypes with unique clinical profiles, molecular signatures, and cognitive terms, we provide compelling evidence for the neurobiological heterogeneity underlying ADHD and lay the groundwork for personalized management.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Attention Deficit Disorder
Machine Learning
Morphometrics
Multivariate
Neurotransmitter
STRUCTURAL MRI
Other - Normative Model; Connectome; Graph Theory
1|2Indicates the priority used for review
Abstract Information
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Kent, J., Lee, N., Peraza, J., Salo, T., Bottenhorn, K., Dockès, J., Blair, R., Oudyk, K., Yu, Y., Nichols, T., Laird, A., Poline, J. B., Yarkoni, T., & De La Vega, A. (2024). Neurosynth Compose: A Free an Open Platform for Precise Large-Scale Neuroimaging Meta-Analysis. Biological Psychiatry, 95(10), S156–S157. https://doi.org/10.1016/j.biopsych.2024.02.376
Rutherford, S., Kia, S. M., Wolfers, T., Fraza, C., Zabihi, M., Dinga, R., Berthet, P., Worker, A., Verdi, S., Ruhe, H. G., Beckmann, C. F., & Marquand, A. F. (2022). The normative modeling framework for computational psychiatry. Nature Protocols, 17(7), 1711–1734. https://doi.org/10.1038/s41596-022-00696-5
Sui, J., Qi, S., van Erp, T. G. M., Bustillo, J., Jiang, R., Lin, D., Turner, J. A., Damaraju, E., Mayer, A. R., Cui, Y., Fu, Z., Du, Y., Chen, J., Potkin, S. G., Preda, A., Mathalon, D. H., Ford, J. M., Voyvodic, J., Mueller, B. A., … Calhoun, V. D. (2018). Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-05432-w
Varol, E., Sotiras, A., & Davatzikos, C. (2017). HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage, 145, 346–364. https://doi.org/10.1016/j.neuroimage.2016.02.041
Wang, H., Jin, X., Zhang, Y., & Wang, J. (2016). Single-subject morphological brain networks: Connectivity mapping, topological characterization and test-retest reliability. Brain and Behavior, 6(4), 1–21. https://doi.org/10.1002/brb3.448