Brain’s Structure-Function Coupling is Organized in Two Anticorrelated Dynamic States
Poster No:
1194
Submission Type:
Abstract Submission
Authors:
Massimiliano Facca1, Alessandra Del Felice1,2, Alessandra Bertoldo1,3
Institutions:
1Padova Neuroscience Center, University of Padova, Padova, Italy, 2Department of Neuroscience, University of Padova, Padova, Italy, 3Department of Information Engineering, University of Padova, Padova, Italy
First Author:
Co-Author(s):
Alessandra Del Felice
Padova Neuroscience Center, University of Padova|Department of Neuroscience, University of Padova
Padova, Italy|Padova, Italy
Padova Neuroscience Center, University of Padova|Department of Neuroscience, University of Padova
Padova, Italy|Padova, Italy
Alessandra Bertoldo
Padova Neuroscience Center, University of Padova|Department of Information Engineering, University of Padova
Padova, Italy|Padova, Italy
Padova Neuroscience Center, University of Padova|Department of Information Engineering, University of Padova
Padova, Italy|Padova, Italy
Introduction:
The relationship between brain structure and function has been described as an "imperfect correspondence", characterized by a progressive decoupling of functional connectivity (FC) from structural connectivity (SC) along the unimodal-to-transmodal axis (Suárez et al. 2020). Recent advancements in the connectome eigenmodes perspective on the structure-function coupling (SFC) problem suggest that low-frequency eigenmodes of SC minimally contribute to functional dynamics in transmodal cortices, which are better explained by high-frequency harmonics of the connectome (Yang et al., 2023). In this study, we challenge these notions by integrating the structural eigenmode perspective with dynamic functional connectivity (dFC) mapping, analyzed through the lens of dynamic states.
Methods:
Structural connectivity (SC) and phase-based, instantaneous dFC (Cabral et al. 2017) were mapped in 100 unrelated subjects from the Human Connectome Project (HCP, Van Essen et al. 2013) Young Adult considering the Schaefer 200 regions 7 Networks atlas (Schaefer at al. 2018). For each time point, every nodal connectivity profile was predicted as a weighted sum of the first N=10 low-frequency eigenmodes of the SC's Laplacian, producing an R2 value for each node and time point (Figure 1a). These R2 time series were concatenated across subjects and analyzed using k-means clustering. We tested a variable number of clusters with K∈{2,…,10}. The optimal number of states was jointly determined using the Silhouette coefficient and the explained variance. The resulting cluster centroids were correlated with maps representing the brain's structural and functional organization, including the T1w/T2w ratio, the first gradient of FC (Margulies et al. 2016), and the first Principal Component of the Allen Human Brain Atlas (AHBA, Hawrylycz et al. 2012). The statistical significance of each correlation was determined using a spin-test to account for spatial autocorrelation (N.nulls = 10,000).
Results:
Silhouette coefficient and explained variance supported K=2 as the optimal K. The two clusters, dSFC state-1 and dSFC state-2, were significantly anticorrelated (r=−0.91, pspin=1×10−4) and dSFC state-1 had slightly greater average dwell time (dwell-timedSFC state-1=2.22±0.42s, dwell-timedSFC state-2=1.63±0.16s) and fractional occupancy (occdSFC state-1=0.57±0.05, occdSFC state-2=0.43±0.05) across subjects. As shown in Figure 2, dSFC state-1 was positively associated with T1w/T2w ratio (r=0.6, pspin=1×10−4) and 1st PC of the AHBA (r=0.64, pspin=1×10−4) and negatively with the 1st FC gradient (r=−0.86, pspin=1×10−4), while dSFC state-2 showed the opposite pattern, characterized by negative correlations with T1w/T2w ratio (r=−0.53, pspin=1×10−4) and 1st PC of the AHBA (r=−0.47, pspin=2×10−4) and a positive association with the 1st FC gradient (r=0.84, pspin=1×10−4).
Conclusions:
Our findings demonstrate that structure-function coupling (SFC) is not consistently higher in the unimodal cortex compared to the transmodal cortex. Instead, it undergoes a complete pattern reversal, occurring systematically over time. This suggests that the way the structural scaffolding mediates function may be low-dimensional and organized into two distinct dynamic SFC (dSFC) states, each exhibiting marked segregation from both functional and structural perspectives. Contrary to the prevailing view that low-frequency eigenmodes better explain the functional profile of the unimodal cortex than the transmodal cortex under any circumstances, our results highlight a temporal inversion of these patterns. This points to multiplexed spreading strategies operating atop the structural connectome.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Keywords:
FUNCTIONAL MRI
Tractography
Other - structure function coupling; multimodal neuroimaging; hierarchy; connectome; dynamic functional connectivity;
1|2Indicates the priority used for review
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2. Hawrylycz, M. J. et al. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391–399. https://doi.org/10.1038/nature11405
3. Margulies, D. S. et al. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574–12579. https://doi.org/10.1073/pnas.1608282113
4. Schaefer, A. et al. (2017). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179
5. Suárez, L. E. et al. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302–315. https://doi.org/10.1016/j.tics.2020.01.008
6. Van Essen, D. C. et al. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
7. Yang, Y. et al. (2023). Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-42053-4