Eigen-microstate analysis revealed functional connectivity alterations in autism spectrum disorder
Poster No:
259
Submission Type:
Abstract Submission
Authors:
Tianhang Liu1, Xi Chen1, Haoda Ren1, Xuhong Liao1
Institutions:
1School of Systems Science, Beijing Normal University, Beijing, China
First Author:
Co-Author(s):
Introduction:
Autism spectrum disorder (ASD) is a typical neurodevelopmental disorder characterized by impairments in social communications and restricted and repetitive patterns in behavior (Lord, 2020). Previous neuroimaging studies have revealed altered functional connectivity patterns in individuals with ASD, mainly involved in the default-mode and sensorimotor systems (Di Martino, 2014a; Hull, 2017). Compared to traditional static (i.e., time-constant) connectivity analysis, the recently developed time-varying functional network analysis can provide richer information about the dynamic coordination of functional networks (Preti, 2017). However, most existing studies in ASD focus on temporal statistics (e.g., variability or transitions) of dynamic connectivity patterns (Xie, 2022). The latent connectivity patterns in dynamic functional networks and their alterations in ASD still need further elucidation.
To address these questions, we used a statistical eigen-microstate analysis on resting-state functional MRI (R-fMRI) data (Chen, 2023) to identify basic activity patterns and latent connectivity patterns in ASD children.
To address these questions, we used a statistical eigen-microstate analysis on resting-state functional MRI (R-fMRI) data (Chen, 2023) to identify basic activity patterns and latent connectivity patterns in ASD children.
Methods:
We used preprocessed R-fMRI data of 45 children (all males, age range: 6.5-10.9 years) selected from the NYU site of ABIDE I (Di Martino, 2014b) with a strict screening based on age, IQ, head motion, and other factors. Based on the preprocessed data of each child, we first extracted time courses of 1000 cortical nodes (Schaefer, 2018) and normalized each nodal time course in terms of Z-score values. Then, we concatenated the normalized time courses of all children within each group (ASD or TD), and performed the eigen-microstate analysis for each group (Chen, 2023). This analysis identified several leading modes (LMs) of brain activity, which dominantly contributed for spontaneous fluctuations over time. Next, for each LM, we calculated the mode-specific internodal coactivation patterns, reflecting the latent connectivity patterns at rest.
For between-group comparison, we matched the LMs between the two groups based on the spatial similarity, and explored the between-group differences in coactivation patterns for each LM (10,000 permutations to test for significance). First, based on a prior template (Yeo, 2011), we examined the between-group differences at the network level, focusing on the intra-network and inter-network coactivation. Second, for LMs that showed network-level alterations, we further identified significantly altered coactivation at the connectivity level and summarized the significantly altered connectivity number to each node.
For between-group comparison, we matched the LMs between the two groups based on the spatial similarity, and explored the between-group differences in coactivation patterns for each LM (10,000 permutations to test for significance). First, based on a prior template (Yeo, 2011), we examined the between-group differences at the network level, focusing on the intra-network and inter-network coactivation. Second, for LMs that showed network-level alterations, we further identified significantly altered coactivation at the connectivity level and summarized the significantly altered connectivity number to each node.
Results:
Based on the eigen-microstate analysis, the weights of different basic modes decreased rapidly with increasing ranking. Six leading basic modes dominantly contributed to spontaneous activity in both groups with the elbow point analysis (Fig. 1A, B). These LMs showed a high spatial similarity between two groups (all rs > 0.75) with one-to-one match, except for a reversal of LMs 3 and 4 (Fig. 1C). Each LM showed a distinct brain activity pattern (Fig. 1D) and corresponded to a functional system-dependent coactivation pattern. For example, LM1 mainly reflected separation of activity (i.e., anti-correlation) between the default-mode network and the somatomotor and ventral attention networks.
Compared to TD children, children with ASD showed significantly altered coactivation patterns for LMs 1, 2, and 4, mainly involving co-activation within the visual network and that between the dorsal attention network and the frontoparietal, default-mode, and visual networks (FDR corrected p < 0.05, Fig. 2A). At the region level, the default-mode, frontoparietal, visual, and attentional networks-related regions showed LM-specific alterations in co-activation (Fig. 2B).
Compared to TD children, children with ASD showed significantly altered coactivation patterns for LMs 1, 2, and 4, mainly involving co-activation within the visual network and that between the dorsal attention network and the frontoparietal, default-mode, and visual networks (FDR corrected p < 0.05, Fig. 2A). At the region level, the default-mode, frontoparietal, visual, and attentional networks-related regions showed LM-specific alterations in co-activation (Fig. 2B).
Conclusions:
These findings suggest that multiple abnormal connectivity patterns simultaneously co-occur in the functional brain networks of ASD children, providing a novel perspective for understanding the pathological mechanisms of ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Other Methods
Keywords:
Autism
FUNCTIONAL MRI
Other - Children; Eigen-microstate analysis; Functional connectivity;
1|2Indicates the priority used for review
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Provide references using APA citation style.
Di Martino A. (2014a). Unraveling the miswired connectome: a developmental perspective. Neuron, 83(6), 1335-1353.
Di Martino A. (2014b). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667.
Hull J.V. (2017). Resting-state functional connectivity in autism spectrum disorders: a review. Frontiers in Psychiatry, 7, 205.
Lord, C. (2020). Autism spectrum disorder. Nature Reviews Disease Primers, 6(5).
Preti M.G. (2017). The dynamic functional connectome: state-of-the-art and perspectives. NeuroImage, 160, 41-54.
Schaefer A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095.
Xie Y. (2022). Alterations in connectome dynamics in autism spectrum disorder: a harmonized mega- and meta-analysis study using the autism brain imaging data exchange dataset. Biological Psychiatry, 91(11), 945-955.
Yeo B.T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.