TY - JOUR
T1 - Dynamic trajectories of connectome state transitions are heritable
AU - Jun, Suhnyoung
AU - Alderson, Thomas H.
AU - Altmann, Andre
AU - Sadaghiani, Sepideh
N1 - Funding Information:
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank Jaime Derringer for her insight and interpretation of quantitative genetic modeling results and Lili Sahakyan for her insightful feedback on the manuscript. AA holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was partly supported by the Medical Research Council (grant number MR/L016311/1). This work was partly supported by the National Institute for Mental Health (1R01MH116226 to Sepideh Sadaghiani). HCP datasets are available via (https://db.humanconnectome.org) and also https://registry.opendata.aws/hcp-openaccess/. HMM codes are publicly available from HMM-MAR (multivariate autoregressive) toolbox (https://github.com/OHBA-analysis/HMM-MAR).
Funding Information:
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank Jaime Derringer for her insight and interpretation of quantitative genetic modeling results and Lili Sahakyan for her insightful feedback on the manuscript. AA holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship. This work was partly supported by the Medical Research Council (grant number MR/L016311/1 ). This work was partly supported by the National Institute for Mental Health (1R01MH116226 to Sepideh Sadaghiani).
Publisher Copyright:
© 2022
PY - 2022/8/1
Y1 - 2022/8/1
N2 - The brain's functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly, Fractional Occupancy (FO) and Transition Probability (TP), representing the duration spent in each connectivity configuration and the frequency of shifting between configurations, respectively. These effects were robust against methodological choices of number of states and global signal regression. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2=0.39, 95% CI= [.24,.54] for FO; h2=0.43, 95% CI=[.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the dynamic trajectory of state transitions (captured by FO and TP), and such temporal features may act as endophenotypes for cognitive abilities.
AB - The brain's functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly, Fractional Occupancy (FO) and Transition Probability (TP), representing the duration spent in each connectivity configuration and the frequency of shifting between configurations, respectively. These effects were robust against methodological choices of number of states and global signal regression. Genetic effects explained a substantial proportion of phenotypic variance of these features (h2=0.39, 95% CI= [.24,.54] for FO; h2=0.43, 95% CI=[.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the dynamic trajectory of state transitions (captured by FO and TP), and such temporal features may act as endophenotypes for cognitive abilities.
KW - Dynamic functional connectivity
KW - Heritability
KW - Hidden markov modeling
KW - Twin study
KW - Variance component modeling
UR - http://www.scopus.com/inward/record.url?scp=85129871976&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129871976&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2022.119274
DO - 10.1016/j.neuroimage.2022.119274
M3 - Article
C2 - 35504564
AN - SCOPUS:85129871976
VL - 256
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
M1 - 119274
ER -