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Iva Ilioska, Marianne Oldehinkel, Alberto Llera, Maroš Rovný, Ting Mei, Seyed Mostafa Kia, Dorothea L. Floris, Julian Tillmann, et al.
(2026).
Multiscale heterogeneity of atypical functional connectivity in autism.
Nature Mental Health, 4(6), 1010-1020.
Springer Science and Business Media LLC.
- Date de publication
-
01/06/2026
- Identifiant
-
10.1038/s44220-026-00656-y
- Auteurs
-
Iva Ilioska,
Marianne Oldehinkel,
Alberto Llera,
Maroš Rovný,
Ting Mei,
Seyed Mostafa Kia,
Dorothea L. Floris,
Julian Tillmann,
Rosemary J. Holt,
Eva Loth,
Tony Charman,
Declan G. M. Murphy,
Christine Ecker,
Tobias Banaschewski,
Maarten Mennes,
Christian F. Beckmann,
Andre Marquand,
Jan K. Buitelaar,
Alex Fornito
- Source
- Nature Mental Health
- Détails
- 4(6), 1010-1020
- Type de référence
- article
- Éditeur
- Springer Science and Business Media LLC
- Source de métadonnées
- crossref
Résumé
Abstract Group-mean comparisons often identify atypical functional connectivity in autism, but it remains unclear whether these findings consistently manifest at the individual level. Here we use normative modeling to quantify the interindividual heterogeneity of atypical functional connectivity across multiple brain scales using multicenter resting-state functional magnetic resonance imaging data from 1,824 participants (796 autistic individuals and 1,028 neurotypical controls) in a cross-sectional study across 32 sites. We find that no single functional connectivity estimate showed extreme deviation from normative expectations in more than 4% of people in either group. However, these deviations converged on common regions and networks in autistic people, who showed up to double the level of overlap compared with controls. Specifically, autistic participants demonstrated convergent hypoconnectivity in sensorimotor and attention regions and convergent hyperconnectivity between frontoparietal and default mode networks. Functional connectivity deviation patterns significantly predicted social and cognitive abilities. These findings demonstrate that autism exhibits scale-dependent heterogeneity, characterized by normative variability at the connection level but significant convergence at regional and network scales. These convergent regions and networks may be used to identify targets for individualized therapeutic development.
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