Cortical similarity networks reveal functional connection abnormalities in children with autism
Poster No:
290
Submission Type:
Abstract Submission
Authors:
Miaoyan Wang1, JIAHE Wang2, YANYAO DU2, Jingjing Qiao1, XINMENG YU3, Gang Li4, Haoxiang Jiang1
Institutions:
1Affiliated Children's Hospital of Jiangnan University, Wuxi, Jiangsu, 2University of North Carolina at Chapel Hill, Chapel Hill, NC, 3Affiliated Children's Hospital of Jiangnan University, Wuxi, China, 4UNC-CH, CHAPEL HILL, NC
First Author:
Co-Author(s):
Introduction:
Autism spectrum disorder (ASD) is commonly associated with abnormalities in functional connectivity. While previous studies have predominantly employed voxel-based analysis methods, surface-based analysis offers a more precise depiction of sulcal and gyral structures in the brain. Recently, Morphometric Inverse Divergence (MIND) has emerged as a novel approach for evaluating the similarity between cortical regions, potentially serving as a new imaging biomarker for the early identification of ASD.
Methods:
This study included 192 children with ASD) and 46 typically developing (TD) children aged 1 to 10 years (Table 1A). All participants underwent MRI scans using a Siemens 1.5T system. Scanning parameters included a 3D magnetization-prepared rapid gradient echo (3D-MPRAGE) T1-weighted imaging sequence (TR = 2200 ms, TE = 3.06 ms, slice thickness = 1.0 mm, no inter-slice gap) and functional MRI (fMRI) (TR = 2000 ms, TE = 25 ms, slice thickness = 3.0 mm). First, surface-based analysis methods were applied to fMRI data to extract functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), and regional homogeneity (ReHo) features, followed by group comparisons with Bonferroni correction. Second, an FC-MIND network was constructed, and the Network-Based Statistic (NBS) method was used to compare FC strength at the network level between groups. Finally, graph theory analysis was conducted to evaluate group differences in the topological properties of the FC-MIND network, with sex and age included as covariates. A significance threshold of P < 0.05 was set for statistical analysis.
Results:
Surface-based analysis revealed that, compared to the HC group, children with ASD exhibited reduced ALFF and ReHo values in the prefrontal and temporal lobes (Figure 2A). NBS analysis of FC edges showed significantly decreased FC strength between multiple networks and their nodes in the ASD group compared to controls (Figure 2B). The affected network connections primarily included the left hemisphere with the limbic network (LIM)-dorsal attention network (DAT), the frontoparietal network (FPN)-default mode network (DMN), and the DMN-DMN, and the right hemisphere with the somatomotor network (SOM)-SOM, DMN-DMN, and the ventral attention network (VAT)-VAT. Increased functional connectivity strength in the ASD group was observed in the following connections: the left hemisphere with the SOM-DMN, the DAT-DMN, and the visual network (VIS)-DAT; and the right hemisphere with the SOM-DMN, the VAT-DMN, and the FPN-SOM. Additionally, both increased and decreased FC strength were observed between hemispheres. NBS analysis based on the FC-MIND network showed that, compared to the HC group, the ASD group exhibited distinct similarities in the DMN, DAT, and VAT networks. Notably, interhemispheric FC strength was significantly reduced between the left DMN and right DAT, as well as between the right DMN and left VAT (Figure 2C). Graph theory analysis of the FC-MIND network revealed increased Gamma and Sigma values in the ASD group compared to the HC group, indicating altered small-world properties (Table 1B).
·Table1A: Demographic Information Table, Table1B: Graph Theory Analysis Based on FC-MIND
·Figure 1 Results of Network-Based Statistic and Graph Theory Analysis. Panel A shows brain regions with differences between Surface-based ASD and TD. Panel B presents the brain network FC strength dif
Conclusions:
The abnormal patterns of morphological similarity in ASD suggest potential impairments in information integration between networks, particularly between the default mode network and the dorsal and ventral attention networks. These findings provide new insights into the macroscopic structural abnormalities associated with the onset and progression of ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Higher Cognitive Functions Other
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Autism
Data analysis
FUNCTIONAL MRI
MRI
PEDIATRIC
1|2Indicates the priority used for review
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