Poster No:
1473
Submission Type:
Abstract Submission
Authors:
Yen-Cheng Liu1, Tak Yan Venice NG2, Ashley Jaimes3, Susan Shur-Fen Gau4, Joshua Goh5
Institutions:
1Graduate Institute of Brain and Mind Science, National Taiwan University, Taipei, Taiwan, 2School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, 3Florida State University, Tallahassee, FL, 4National Taiwan University Hospital, Taipei, Taiwan, 5Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taipei
First Author:
Yen-Cheng Liu
Graduate Institute of Brain and Mind Science, National Taiwan University
Taipei, Taiwan
Co-Author(s):
Tak Yan Venice NG
School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong
Hong Kong
Joshua Goh
Graduate Institute of Brain and Mind Sciences, National Taiwan University
Taipei, Taipei
Introduction:
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by disrupted neural connectivity. Recent research has described global functional network connectivity in ASD as having overconnectivity in short-range connections and underconnectivity in long-range connections. Moreover, studies have reported within-network hyperconnectivity and between-network hypoconnectivity in children and adolescents with ASD, indicating stronger local connections and weaker inter-network integration compared to typically developing (TD) individuals. These findings across studies, however, remain inconsistent, highlighting the need for a more systematic spatial and functional characterization of connectivity alterations in ASD. In this study, we note that the human brain connectome balances structural connection cost with functional transmission efficiency. As such, we investigate distinct functional connectivity patterns in ASD and TD individuals across anatomical distances within the gray matter geodesic manifold.
Methods:
We analyzed resting-state functional magnetic resonance imaging (fMRI) data from a sample comprising 89 individuals with ASD (Female: Male = 3:86, mean age±SD: 14.04±2.89, age range: 9.3-26.0) and 104 TD controls (Female: Male = 15:89, mean age±SD: 13.51±3.13, age range: 8.3-19.5) collected in Taiwan. Using T1 MPRAGE, gray matter intervoxel distances were computed using Dijkstra's algorithm, which yields the shortest intervoxel distances based on spatial adjacency weights, constraining distance measures within the gray matter manifold and preserving anatomical characteristics. From the resting fMRI data, Pearson's correlation coefficients between voxel time series were used to represent intervoxel functional connectivity (FC). Generalized additive modeling (GAM) and non-linear curve fitting were applied to parameterize distance x FC relationships per individual. GAM captured data-driven trends in FC over distance using smooth splines with complexity adjusted to avoid overfitting. Curve fitting estimated coefficients for a composite cubic polynomial to capture mid-distance-range trends and logarithmic functions to model short- and long-distance trends. Coefficients of determination (R²) evaluated model performances. Analyses focused on the top 2% and 10% of voxel pairs with the highest FC values to emphasize strong connections.
Results:
Distance-dependent FC distribution analysis for the top 2% and 10% voxel pairs revealed that FCs decreased with increasing distance. Critically, voxel-pairs with FC > 0.6 were more concentrated at short distances (20–40 mm) in ASD, while TD displayed a more balanced distribution across distances. Formally, GAM analysis of top 10% voxel-pairs revealed significantly higher beta coefficients for short-range connections in ASD compared to TD, and trends of lower coefficients for long-range connections. Thus, initial FC declines over distance were steeper in ASD relative to TD. Greater coefficient variability was also observed in ASD, reflecting increased heterogeneity. Composite polynomial model coefficients confirmed these trends, capturing a logarithmic decay at short distances and gradual declines over longer distances in ASD relative to TD, reinforcing the GAM results with more interpretable functions.
Conclusions:
This study reveals distinct distance-dependent functional connectivity patterns in ASD, marked by elevated short-range connectivity and reduced long-range connectivity compared to TD individuals. Both GAM and non-linear curve fitting distinguished structural modulation functional connectome organization. The latter further affords theory-driven interpretations which provides more specific insights to the altered neural architecture underlying ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Autism
FUNCTIONAL MRI
Modeling
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
Haghighat, H., Mirzarezaee, M., Araabi, B. N., & Khadem, A. (2021). Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity. Brain Topography, 34(3), 306-322. https://doi.org/10.1007/s10548-021-00831-7
Long, Z., Duan, X., Mantini, D., & Chen, H. (2016). Alteration of functional connectivity in autism spectrum disorder: effect of age and anatomical distance. Scientific Reports, 6, 26527. https://doi.org/10.1038/srep26527
Nomi, J. S., & Uddin, L. Q. (2015). Developmental changes in large-scale network connectivity in autism. NeuroImage: Clinical., 7, 732-741. https://doi.org/10.1016/j.nicl.2015.02.024
Park, B. Y., Benkarim, O., Weber, C. F., Kebets, V., Fett, S., Yoo, S., Martino, A. D., Milham, M. P., Misic, B., Valk, S. L., Hong, S. J., & Bernhardt, B. C. (2024). Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. Neuroimage, 285, 120481. https://doi.org/10.1016/j.neuroimage.2023.120481
Park, B. Y., Vos de Wael, R., Paquola, C., Lariviere, S., Benkarim, O., Royer, J., Tavakol, S., Cruces, R. R., Li, Q., Valk, S. L., Margulies, D. S., Misic, B., Bzdok, D., Smallwood, J., & Bernhardt, B. C. (2021). Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage, 224, 117429. https://doi.org/10.1016/j.neuroimage.2020.117429
van den Heuvel, M. P., Kahn, R. S., Goni, J., & Sporns, O. (2012). High-cost, high-capacity backbone for global brain communication. Proceedings of the National Academy of Sciences of the United States of America, 109(28), 11372-11377. https://doi.org/10.1073/pnas.1203593109
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