ArticleBrain imaging and behavior2026
Gray matter subtyping with SuStaIn reveals neuroanatomical patterns in autism spectrum disorder.
Article in Brain imaging and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Autism spectrum disorder (ASD) is highly heterogeneous, and structural magnetic resonance imaging (MRI) studies have reported both increased and decreased gray matter volume relative to typically developing controls. This variability may obscure distinct neuroanatomical patterns in conventional group-level analyses. It therefore motivates subtyping approaches that can characterize neuroanatomical heterogeneity beyond conventional group-level analyses. We applied Subtype and Stage Inference (SuStaIn) to structural MRI data from the Autism Brain Imaging Data Exchange (ABIDE) to identify gray matter subtypes and stage-like variation in ASD. Original and sign-reversed z-score representations were modeled to capture gray matter deviations above and below the control reference. We then evaluated whether the organization of subtype-related brain regions was consistent across deviation directions. Cross-direction consistency, within-ABIDE cross-cohort separability, and structural covariance network (SCN) differences between stage groups were assessed using subtype alignment, Dice coefficients, cross-cohort machine-learning classification, and structural covariance analyses. We identified two gray matter subtype patterns in ASD that were consistent across deviation directions. Subtype 1 was centered on cerebellar-limbic-striatal regions, whereas Subtype 2 involved anterior fronto-cingulo-insular regions. In exploratory covariate-adjusted analyses, higher stage in sign-reversed-model S1 was associated with lower verbal intelligence quotient (IQ) scores, whereas S2 showed modestly higher Autism Diagnostic Observation Schedule (ADOS) scores. The two subtypes showed regionally distinct volume profiles. The two-subtype solution showed strong cross-direction agreement, and within-ABIDE cross-cohort classification supported structural separability of the aligned subtypes. In the sign-reversed model, SCN analyses further identified stage-group structural covariance differences within each subtype, with little evidence for a stable covariance difference between subtypes. This study identifies two gray matter-based ASD subtype patterns that were consistent across the original and sign-reversed z-score representations, with distinct regional volume profiles, selective clinical associations, and stage-group structural covariance differences. The findings provide a neuroanatomical framework for organizing ASD heterogeneity and for examining brain-behavior variation in future studies.
Indexed as
Identifiers
42853485What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.