TY - JOUR
T1 - Mapping individual differences across brain network structure to function and behavior with connectome embedding
AU - Levakov, Gidon
AU - Faskowitz, Joshua
AU - Avidan, Galia
AU - Sporns, Olaf
N1 - Funding Information: This research was supported by the U.S.-Israel Binational Science Foundation (BSF), Grant 2017242 to GA and OS. OS was partially supported by NIH Grant R01MH122957 . This material is based upon work supported by the National Science Foundation Graduate Research Fellowshi p under Grant No. 1342962 (J.F.). Publisher Copyright: © 2021 The Authors
PY - 2021/11/15
Y1 - 2021/11/15
N2 - The connectome, a comprehensive map of the brain's anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
AB - The connectome, a comprehensive map of the brain's anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n = 542; Cam-CAN: n = 601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping, are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.
KW - Behavior
KW - Connectome
KW - Functional connectivity
KW - Individual differences
KW - Structural connectivity
UR - http://www.scopus.com/inward/record.url?scp=85112840358&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.neuroimage.2021.118469
DO - https://doi.org/10.1016/j.neuroimage.2021.118469
M3 - Article
C2 - 34390875
SN - 1053-8119
VL - 242
JO - NeuroImage
JF - NeuroImage
M1 - 118469
ER -