Hubs of Dynamic Structure-Function Coupling Align to Uni-/Transmodal Principle Functional Gradient
Poster No:
1614
Submission Type:
Abstract Submission
Authors:
Marlena Duda1, Oktay Agcaoglu2, Zening Fu3, Jiayu Chen4, Vince Calhoun5
Institutions:
1Georgia State University, Atlanta, GA, 2Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, 3Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg, Atlanta, GA, 4GSU, Atlanta, GA, 5GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Oktay Agcaoglu, PhD
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Zening Fu
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg
Atlanta, GA
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georg
Atlanta, GA
Introduction:
Neuroimaging measures of brain structure and function show important interdependence related to cognition, development, and psychopathology. While functional connectivity patterns in the brain are known to vary on both short (seconds-minutes) and long (weeks+) timescales, structural measures (e.g., gray matter volume; GMV) are considered static across short timescales and variations are only appreciated across longer periods. Recently, dynamic fusion (DF) approaches have been developed to capture time-resolved linkages between brain structure and dynamic functional network connectivity (dFNC), which can reflect changing cognitive and environmental demands (Duda 2024). We utilize the DF framework to investigate the nature of time-resolved structure-function (S-F) coupling in healthy brains and the organization of the cortex into regions of static/dynamic linkages.
Methods:
We analyzed T1 sMRI and resting-state fMRI (rs-fMRI) scans (Rest1_LR and Rest2_LR; R1 and R2) from the HCP 1200 (Van Essen 2013) (N = 833 subs, avg age = 28.7 years). We computed sliding window dFNC in sessions R1 and R2 separately, resulting in 5 dFNC states each via k-means. DF involves conducting multiple linked fusions (mCCA + jICA; model order = 15) holding structural inputs (GMV maps) constant and varying functional inputs (subject-level dFNC) for each of the states–thus we ran 10 total fusions (5 states x 2 sessions) with 150 total components (10 fusions x 15 components). For each session, stability of component maps was assessed by pairwise greedy matching of components for all state-wise fusions, and utilized intra-state comparisons as baselines. We averaged the cross-fusion stability for each (75) component per session and generated both static and dynamic component maps as weighted averages of the 10 most static and dynamic (i.e. highest and lowest stability) components. We subtracted dynamic maps from static, and resultant differential stability maps were averaged across R1 and R2 experiments to obtain a global view of brain organization into regions of static/dynamic S-F coupling.
Results:
Experimental inter-state results showed significantly lower cross-fusion stability than intra-state baselines for both dFNC and GMV components, indicating specialization of S-F linkages given changing functional contexts (dFNC states). The differential stability map shows organization of the brain into clear hubs of static and dynamic S-F coupling, as well as mixed regions that exhibit both properties. In the structural map static hubs are composed mostly of anterior cingulate and visual regions, whereas dynamic hubs are found in the insula, default mode, and subcortical regions. These patterns are also reflected in the functional map–the insula, inferior parietal lobule, posterior cingulate, precuneus and subcortical structures are all marked by stronger edges in dynamic components, whereas visual, sensorimotor and anterior cingulate regions all serve as hubs for edges in predominantly static joint sources.
·Cross-fusion stability of structural and functional component maps. Static/dynamic threshold r = 0.50 shown in dotted line.
·Differential stability maps in both structural and functional components show organization of brain along static/dynamic coupling gradient
Conclusions:
Our results support the use of DF to identify functionally adaptive structural basis sets related to changing functional contexts. We underscore that the structural inputs are identical at each independent fusion, thus any change in the resultant structural component maps is driven solely by its varying linkage to the changing functional inputs at each state. Our global differential stability map echos recent results (Liu 2022) and aligns well to the unimodal-transmodal principle functional gradient (Margulies 2016), where static hubs align to extremes of unimodal (visual)-transmodal (association cortex) structures, whereas dynamic hubs exist in intermediary regions of this gradient. Some posit the unimodal-transmodal gradient reflects connection lengths across the cortex. Thus, the stability at the poles of this gradient could reflect this–speculatively, stemming from the ease of maintaining shorter connections and the importance of stabilizing vital long-range connections.
Modeling and Analysis Methods:
Multivariate Approaches 1
Task-Independent and Resting-State Analysis 2
Keywords:
ADULTS
FUNCTIONAL MRI
Machine Learning
Multivariate
NORMAL HUMAN
Statistical Methods
STRUCTURAL MRI
Other - Multimodal Data Fusion; Dynamic Functional Network Connectivity; Structure-Function Coupling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Liu, Z. Q., Vazquez-Rodriguez, B., Spreng, R. N., Bernhardt, B. C., Betzel, R. F., & Misic, B. (2022). Time-resolved structure-function coupling in brain networks. Communications biology, 5(1), 532.
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., ... & Smallwood, J. (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.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Wu-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.