English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles

MPS-Authors
/persons/resource/persons71665

Valk,  Sofie L.
Otto Hahn Group Cognitive Neurogenetics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute of Neuroscience and Medicine, Research Center Jülich, Germany;
Institute of Systems Neuroscience, University Hospital Düsseldorf, Germany;

External Resource
No external resources are shared
Fulltext (public)

Choi_2022.pdf
(Publisher version), 4MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Choi, H., Byeon, K., Park, B.-y., Lee, J.-e., Valk, S. L., Bernhardt, B., et al. (2022). Diagnosis-informed connectivity subtyping discovers subgroups of autism with reproducible symptom profiles. NeuroImage, 256: 119212. doi:10.1016/j.neuroimage.2022.119212.


Cite as: http://hdl.handle.net/21.11116/0000-000A-6510-2
Abstract
Clinical heterogeneity has been one of the main barriers to develop effective biomarkers and therapeutic strategies in autism spectrum disorder (ASD). Recognizing this challenge, much effort has been made in recent neuroimaging studies to find biologically more homogeneous subgroups (called ‘neurosubtypes’) in autism. However, most approaches have rarely evaluated how much the employed features in subtyping represent the core anomalies of ASD, obscuring its utility in actual clinical diagnosis. To address this, we combined two data-driven methods, ‘connectome-based gradient’ and ‘functional random forest’, collectively allowing to discover reproducible neurosubtypes based on resting-state functional connectivity profiles that are specific to ASD. Indeed, the former technique provides the features (as input for subtyping) that effectively summarize whole-brain connectome variations in both normal and ASD conditions, while the latter leverages a supervised random forest algorithm to inform diagnostic labels to clustering, which makes neurosubtyping driven by the features of ASD core anomalies. Applying this framework to the open-sharing Autism Brain Imaging Data Exchange repository data (discovery, n = 103/108 for ASD/typically developing [TD]; replication, n = 44/42 for ASD/TD), we found three dominant subtypes of functional gradients in ASD and three subtypes in TD. The subtypes in ASD revealed distinct connectome profiles in multiple brain areas, which are associated with different Neurosynth-derived cognitive functions previously implicated in autism studies. Moreover, these subtypes showed different symptom severity, which degree co-varies with the extent of functional gradient changes observed across the groups. The subtypes in the discovery and replication datasets showed similar symptom profiles in social interaction and communication domains, confirming a largely reproducible brain-behavior relationship. Finally, the connectome gradients in ASD subtypes present both common and distinct patterns compared to those in TD, reflecting their potential overlap and divergence in terms of developmental mechanisms involved in the manifestation of large-scale functional networks. Our study demonstrated a potential of the diagnosis-informed subtyping approach in developing a clinically useful brain-based classification system for future ASD research.