Associations between functional connectome asymmetry and symptom severity in autism
Poster No:
1210
Submission Type:
Abstract Submission
Authors:
Chae Yeon Kim1, Bo-yong Park2
Institutions:
1Inha University, Incheon, Incheon, 2Korea University, Seoul, Seoul
First Author:
Co-Author:
Introduction:
Autism spectrum disorder is a common neurodevelopmental condition characterized by atypical sensory and cognitive processing (DSM-5, 20172013). Brain asymmetry has been linked to symptom severity in various neurological and psychiatric conditions (Wang et al., 2023). In studies of autism, altered asymmetry in brain morphology and connectivity has been observed (Park et al., 2021; ); however, the relationship between these asymmetry patterns and the severity of autistic symptoms has not been systematically examined. In this study, we aimed to identify the link between functional connectome asymmetry and autism symptoms, as measured by the Autism Diagnostic Observation Schedule (ADOS) (Lord et al., 2000), using multivariate analysis.
Methods:
We analyzed T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional MRI (fMRI) data from the Autism Brain Imaging Data Exchange I initiative (Di Martino et al., 2014). The sample consisted of 159 individuals with autism (mean age ± standard deviation [SD] = 17.4 ± 8.2 years; 12% female) and 22 healthy controls (mean age ± SD = 20.6 ± 6.1 years; 0% female). Participants who did not complete ADOS assessments, failed to reconstruct cortical surface and subcortical regions, or had improper registration to a standard template were excluded. Imaging data were preprocessed using micapipe (Cruces et al., 2022). Functional connectivity matrices were constructed by calculating Pearson's correlations of time series between brain regions defined by the Schaefer atlas with 300 parcels (Schaefer et al., 2018). The resulting correlation coefficients were transformed using Fisher's r-to-z method to ensure a normal distribution (Thompson , 2016). Betweenness centrality (BC), which represents the proportion of shortest paths between all node pairs in a network that pass through a given node, was computed (Rubinov, 2010) (Fig. 1A). The inter-hemispheric asymmetry of BC was calculated as follows: asymmetry = (left-right) / {(left+right)/2}, where left and right indicate BC values for the left and right hemispheres, respectively (Fig. 1B). While controlling for site, age, and sex, we conducted partial least squares (PLS) regression to assess the relationship between BC asymmetry and ADOS sub-scores (communication, social cognition, and repetitive behavior/interest) (McIntosh, 2013)
Results:
BC showed left-dominant asymmetry in the frontoparietal and default mode networks, as well as the hippocampus, and right-dominant asymmetry in the somatomotor and temporal regions (Fig. 1B). PLS scores (X and Y scores) were calculated by projecting the data onto loading vectors, with the first component showing a significant association (r = 0.452, p < 0.001; Fig. 1C). The loadings indicated that increased functional connectivity asymmetry in the visual and dorsal attention networks, as well as the thalamus, was related to higher autistic symptom severity, particularly in communication. On the other hand, decreased asymmetry in the somatomotor, ventral attention, and default mode networks, as well as the amygdala, was linked to higher ADOS scores.
·Fig 1 | Association between functional connectome asymmetry and symptom severity in autism spectrum disorder (ASD).
Conclusions:
Through multivariate association analysis, we uncovered significant links between the asymmetry of functional connectome organization and symptom severity in individuals with autism. The regions identified are potentially related to executive and language functions, highlighting the need for further investigation in future studies. Our findings provide insights into the relationship between functional connectome asymmetry and autistic symptoms.
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
Keywords:
Autism
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Multivariate
Psychiatric
Other - asymmetry; functional connectivity
1|2Indicates the priority used for review
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