The Heritability of Edge-centric Functional Network in Human Brain Activity
Poster No:
1829
Submission Type:
Abstract Submission
Authors:
Yuanyuan Hu1,2, Yuening Jin1,2, Qingchen Fan1,2, Yuan Zhou1,2,3
Institutions:
1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 2Department of Psychology, University of Chinese Academy of Sciences, Beijing, China, 3The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
First Author:
Yuanyuan Hu
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Yuening Jin
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Qingchen Fan
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Yuan Zhou
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences|The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University
Beijing, China|Beijing, China|Beijing, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences|The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University
Beijing, China|Beijing, China|Beijing, China
Introduction:
Intrinsic patterns of human brain activity reveal a functional connectivity profile that is intricately linked to behavior, cognitive performance, and disease progression. However, traditional network neuroscience approaches have primarily relied on a node-centric model, which assigns each brain node to a single community, limiting our understanding of interactions among edges and fluctuations of connectivity across time (Betzel et al., 2023). Recently, an edge-centric network model has been proposed to address the aforementioned issues by dividing the cerebral cortex into overlapping communities through examining its edge correlation structure and thus this approach offers new insights into human brain activity (Faskowitz et al., 2020). In the current study, for the first time, we estimate the heritability of the overlapping community features of edge-centric functional connectivity (eFC) and discern the relative contributions of genetic factors and the shared environment among twins.
Methods:
A cohort of 102 pairs of same-sex twins was included in the analyses. Their ages ranged from 18 to 24 years (M = 19.60 years, SD = 1.68). Among them, 47 pairs were monozygotic (23 male and 24 female pairs), and 55 pairs were dizygotic (25 male and 30 female pairs). Functional images of this study were preprocessed using fMRIPrep 23.0.2 (Esteban et al., 2019) and were post-processed and noise-regressed by XCP-D (Mehta et al., 2023). Functional regions were defined based on the Schaefer 200 atlas (Schaefer et al., 2018).
We calculated the eFC matrix and applied a modified k-means algorithm (k = 10, repeated 250 times) to partition the eFC matrix into non-overlapping communities of co-fluctuating edges. Edge assignments were then mapped back to a single region to calculate normalized entropy, providing overlapping regional community assignments. We then employed a univariate model implemented in the OpenMx package (Boker et al., 2011) in the R programming environment (Version 3.1.2) to estimate the means and genetic (A), shared environmental (C), and nonshared environmental (E) sources of variance for the normalized entropy for each brain region (Figure 1).
We calculated the eFC matrix and applied a modified k-means algorithm (k = 10, repeated 250 times) to partition the eFC matrix into non-overlapping communities of co-fluctuating edges. Edge assignments were then mapped back to a single region to calculate normalized entropy, providing overlapping regional community assignments. We then employed a univariate model implemented in the OpenMx package (Boker et al., 2011) in the R programming environment (Version 3.1.2) to estimate the means and genetic (A), shared environmental (C), and nonshared environmental (E) sources of variance for the normalized entropy for each brain region (Figure 1).
Results:
We found that normalized entropy, a measure of eFC community overlap, exhibits heritability in multiple regions. Notably, the sensorimotor regions displayed the highest heritability (41%, 95%CI = [0.18, 0.59]). We also found a considerable and significant heritability within the default mode regions (20% - 29%), the visual regions (21% - 26%), the control related regions (18% - 28%) (Figure 2.). These findings suggest a substantial genetic influence on the distribution of eFC community affiliations within specific brain regions.
Conclusions:
In conclusion, our investigation employed an edge-centric network model to examine human brain connectivity beyond individual nodes, offering fresh insights into the heritability of the functional connectivity profile. The identified moderate heritability highlights the genetic impact on community overlap, specifically within the sensorimotor regions. These findings enhance our comprehension of the intricate interplay between genetic and environmental factors in shaping the functional connectivity of the brain.
Genetics:
Genetic Modeling and Analysis Methods 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
FUNCTIONAL MRI
Other - Edge-centric Functional network; Resting-state Functional Magnetic Resonance Imaging; Heritability; Twin Study
1|2Indicates the priority used for review
Provide references using author date format
Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., Spies, J., Estabrook, R., Kenny, S., Bates, T., Mehta, P., & Fox, J. (2011). OpenMx: An Open Source Extended Structural Equation Modeling Framework. Psychometrika, 76(2), 306–317. https://doi.org/10.1007/s11336-010-9200-6
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., & Snyder, M. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.
Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020). Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nature Neuroscience, 23(12), 1644–1654.
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