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Individualized morphometric-similarity deviations in autism linked to cortical hierarchy and microarchitecture

nature.com 22.10.2026 02:00 4 views

Autism spectrum disorder (ASD) shows marked interindividual variation that complicates efforts to identify coherent neuroanatomical signatures. Here we combine subject-wise morphometric-similarity networks with hierarchical Bayesian normative regression to map per-region, per-subject departures from expected cortical organization across 308 parcels in a large multisite cohort. Normative z-maps reveal that ASD individuals have a higher prevalence of extreme, spatially focal deviations than controls, yet fewer than 12% of cases show extreme deviation in any single region.

These deviations are not random: they preferentially redistribute morphometric similarity from association and heteromodal cortex toward sensory and visual territories and align with canonical cortical gradients. Network-level analyses identify a spatially localized subnetwork of altered similarity (dominant in occipito-temporal/visual nodes). Semi-supervised clustering of individual z-maps resolves a reproducible continuous axis of deviation whose opposite ends carry opposite global polarity and distinct cortical-molecular couplings revealed by transcriptomic and neurotransmitter-receptor density maps, as well as divergent cognitive meta-analytic associations.

Together, these findings reframe ASD neuroanatomy as gradient-constrained and molecularly tethered at the individual level and support normative, subject-wise approaches for mechanism-informed stratification. The authors thank all study participants and investigators contributing to the ABIDE consortium. This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China Department of Human Anatomy, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China Correspondence to Yiyong Chen or Lin Li. The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Mamat, M., Chen, Y. & Li, L.

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