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

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2022-08, Vol.256, p.119212-119212, Article 119212
• Main Authors: Choi, Hyoungshin, Byeon, Kyoungseob, Park, Bo-yong, Lee, Jong-eun, Valk, Sofie L., Bernhardt, Boris, Martino, Adriana Di, Milham, Michael, Hong, Seok-Jun, Park, Hyunjin
• Format: Article
• Language: English
• Subjects: Autism; Brain research; Clustering; Cognitive ability; Datasets; Diagnosis; Functional random forest; Gradient; Magnetic resonance imaging; Medical imaging; Neural networks; Neurobiology; Neuroimaging; Neurosciences; Neurosubtypes; Replication; Reproducibility; Supervised-unsupervised hybrid clustering; Variables
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119212

•Here, we implemented a novel neurosubtyping framework for autism spectrum disorder based on the intrinsic functional connectivity gradients.•This approach leveraging a supervised algorithm identified ASD-specific gradient profiles and fed them into the clustering method.•The symptom severity in the identified subtypes was largely reproducible between two independent datasets.•There were both overlapping and divergent brain phenotypes between neurosubtypes in ASD and typically developing groups. 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 pres