Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism

•We studied structure-function coupling in autism using a Riemannian optimization.•Higher coupling was observed when polysynaptic mechanisms were accounted for.•Compensation was lower in autism, particularly in transmodal association systems.•Structure-function differences in autism reflected autist...

Full description

Saved in:
Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 285; p. 120481
Main Authors: Park, Bo-yong, Benkarim, Oualid, Weber, Clara F., Kebets, Valeria, Fett, Serena, Yoo, Seulki, Martino, Adriana Di, Milham, Michael P., Misic, Bratislav, Valk, Sofie L., Hong, Seok-Jun, Bernhardt, Boris C.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.01.2024
Elsevier Limited
Elsevier
Subjects:
Autism
Autism Spectrum Disorder
Autistic Disorder - diagnostic imaging
Brain
Brain Mapping - methods
Communication
Connectome - methods
Diffusion time
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Neural networks
Neuroimaging
Predictions
Radiology/Diagnostic Imaging
Scanners
Structure-function coupling
Structure-function relationships
Synaptic communication
Structure-function coupling
Autism
Diffusion time
Synaptic communication
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•We studied structure-function coupling in autism using a Riemannian optimization.•Higher coupling was observed when polysynaptic mechanisms were accounted for.•Compensation was lower in autism, particularly in transmodal association systems.•Structure-function differences in autism reflected autistic symptoms intelligence. Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2023.120481