Hierarchical disorganization in structure-function coupling of brain connectomes in autism
Poster No:
1201
Submission Type:
Abstract Submission
Authors:
TAEYEON KIM1, Bo-yong Park2
Institutions:
1INHA University, Incheon, Republic of Korea, 2Korea University, Seoul, Republic of Korea
First Author:
Co-Author:
Introduction:
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in communication, behavior, and social interactions (APA, 2022). Studies have shown alterations in both structural and functional brain network in ASD, particularly along the hierarchical axis spanning sensory to cognitive control regions (Hong, 2019; Park, 2021). However, the simultaneously disruptions of structural connectivity (SC) and functional connectivity (FC) in ASD remains poorly understood. A recent study introduced a Riemannian optimization-based approach for integrating SC and FC (Benkarim, 2022; Park, 2024). Building on these findings, we hypothesized that ASD would show distinct disruptions in hierarchical structure-function coupling compared to typically developing (TD) controls.
Methods:
We obtained structural, diffusion, and functional magnetic resonance imaging (MRI) from the Autism Brain Imaging Data Exchange II (ABIDE-II) database. Of the 1,045 participants, those with incomplete multimodal MRI data were excluded, leaving 158 participants for the study (ASD: n=91, 11.9±5.2 years, 14.3% female; TD: n=67, 12.8±4.0 years, 4.5% female). Structure-function coupling was analyzed separately for ASD and TD groups using Riemannian optimization with five-fold cross-validation (Benkarim, 2022). SC was projected onto a low-dimensional manifold space via diffusion map embedding across varying diffusion times (t between 1 and 10), where lower t reflects single-synapse transfer and higher t captures multi-synapse communication (Benkarim, 2022). FC was simulated for each diffusion time, and accuracy was measured by calculating linear correlations between the empirical and simulated FC matrices. The first principal component (i.e., gradient) was generated from the simulated FC, and hierarchical distance was calculated by subtracting gradient values of the default mode network from those of the visual and somatomotor networks (Fig. 1A). Diffusion times corresponding to the highest (sFC_HD) and lowest (sFC_LD) hierarchical distance were identified, and gradient changes (∆Gradient=Gradient(sFC_HD )-Gradient(sFC_LD)) were computed. Gradient changes were then compared between the ASD and TD groups.
Results:
Prediction accuracy improved with increasing diffusion times and was higher in the TD group compared to the ASD group (Fig. 1B), suggesting weaker structure-function coupling in ASD. At low diffusion times, individuals with ASD showed a clearer hierarchical structure, but at higher diffusion times, this structure became less pronounced compared to the TD group (Fig. 1A). Between-group comparisons of gradient changes revealed that TD group exhibited greater changes in the default mode and frontoparietal regions, while ASD group showed larger changes in the visual and somatomotor regions (FDR < 0.001; Fig. 1C).
Conclusions:
This study revealed differences in the hierarchical structure of structure-function coupling between ASD and TD groups. Our findings align with previous studies showing atypical sensitivity in sensory and transmodal regions in individuals with ASD (Hong, 2019).
Funding: This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks; RS-2021-II212068, Artificial Intelligence Innovation Hub), and Institute for Basic Science (IBS-R015-D1).
Funding: This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks; RS-2021-II212068, Artificial Intelligence Innovation Hub), and Institute for Basic Science (IBS-R015-D1).
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Motor Behavior:
Motor Behavior Other
Novel Imaging Acquisition Methods:
Anatomical MRI
Diffusion MRI
Keywords:
Autism
Computational Neuroscience
Data analysis
Development
Psychiatric
Other - structrue-function coupling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hong, S.J. (2019). Atypical functional connectome hierarchy in autism. Nature Communications, 10, 1022.
Park, B.Y. (2021). Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism. Nature Communications, 12, 2225.
Benkarim, O. (2022). A Riemannian approach to predicting brain function from the structural connectome. NeuroImage, 257, 119299.
Park, B.Y. (2024). Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. NeuroImage, 285, 120481.