Towards Common Dynamic Connectivity Modes in Schizophrenia and Healthy Controls
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1444
Submission Type:
Abstract Submission
Authors:
David Blair1, Vince Calhoun2
Institutions:
1Georgia State University, Atlanta, GA, 2GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author:
Introduction:
In the fifteen years since its development, dynamic fMRI has provided crucial insights into brain operations, such as functionally distinct spatial networks (Du et al., 2020) and consistent evidence of a low-dimensional connectivity state space (Cabral et al., 2017). Nonetheless, dynamic data remains underutilized due to a lack of data-driven methods which account for the sequential nature of dynamic data. Fortunately, the field of fluid dynamics has addressed similar problems and developed solutions. One such, introduced in 2010, is dynamic mode decomposition (Schmid, 2022), or DMD. DMD may be conceptualized as an eigendecomposition of a system's Koopman operator or as an approximate eigendecomposition of the best-fit linear map between sequential time points; both produce identical results (H. Tu et al., 2014). Crucially, Mezić and Banaszuk have demonstrated that for any finite-time dataset collected from any attractor, the modes are equivalent to a discrete Fourier transform (Mezić, 2005). This implies that the dynamics of the dataset may be reconstructed from a relatively sparse library of modes and frequencies, and thus these modes and frequencies summarize the temporal evolution of the data with minimal initial assumptions. In principle, then, DMD precisely fills the need identified.
Methods:
We identify functionally distinct spatial networks via independent component analysis constrained by the NeuroMark template (Du et al., 2020) (cICA) to the FBIRN Phase III dataset (Glover et al., 2012). We then computed windowed functional network connectivity (wFNC) and searched for recurring, time-dependent patterns between these networks using dynamic mode decomposition (DMD) (Schmid, 2022). DMD estimates the eigendecomposition of the linear map between consecutive samples, thus accounting for temporal dependencies and providing insights into spatial pattern dynamics, oscillation frequency, and power. Note that we applied DMD to concatenated subject data, so the modes obtained represent the entire dataset. We isolate subject-level modes and mode time courses via dual regression, first extracting mode time courses from subject dFNC, then using these time courses to identify the subject-level spatial modes. Subject-level power spectra were derived from the subject-level spatial modes and their frequencies. The six modes with the greatest dataset-level power were selected for further analysis. These modes underwent group comparisons of power spectra and spatial mode structure. To examine spatial structure, subject-level spatial modes were z-scored and each cell of the spatial mode underwent group-level comparison. Group-level comparisons all employed the Kolmogorov-Smirnov two-sample test and underwent false-discovery rate correction.
Results:
All six examined modes showed significant reductions in mean power and variance in schizophrenia. Both real and imaginary components of the six examined modes display high levels of structure, with inter-domain connectivity frequently more pronounced than intra-domain. Notably, real and imaginary components generally have markedly different spatial structure. FDR-corrected Kolmogorov-Smirnov two-sample tests reveal substantial group-level connectivity changes in all but one mode, with changes concentrating between functional domains.
Conclusions:
NeuroMark gICA and DMD isolate inter-domain alterations in spatial and temporal structure between groups. The reduced patient power in the six dominant modes suggests a flattening of the power distribution or an overall power reduction. Connectivity alterations imply issues with inter-domain integration, supporting the dysconnectivity hypothesis (Rolls et al., 2020). Both groups display substantial intra-group heterogeneity, indicating possible transdiagnostic subgroups. Future studies should search for such subgroups, perhaps via generative embedding (Brodersen et al., 2011) or functional connectivity dynamics-specific tests (Seguin et al., 2023; Zalesky & Breakspear, 2015).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Methods Development 2
Task-Independent and Resting-State Analysis
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Machine Learning
Psychiatric
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cabral, J. R. B., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N., & Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific Reports, 7(1), 5135. https://doi.org/10.1038/s41598-017-05425-7
Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol, A., Rahaman, M. A., Chen, J., Hong, L. E., Kochunov, P., Osuch, E. A., & Calhoun, V. D. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375. https://doi.org/10.1016/j.nicl.2020.102375
Glover, G. H., Mueller, B. A., Turner, J. A., … Potkin, S. G. (2012). Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. In Journal of Magnetic Resonance Imaging (Vol. 36, Issue 1, pp. 39–54). https://doi.org/10.1002/jmri.23572
H. Tu, J., W. Rowley, C., M. Luchtenburg, D., L. Brunton, S., & Nathan Kutz, J. (2014). On dynamic mode decomposition: Theory and applications. Journal of Computational Dynamics, 1(2), 391–421. https://doi.org/10.3934/jcd.2014.1.391
Mezić, I. (2005). Spectral Properties of Dynamical Systems, Model Reduction and Decompositions. In Nonlinear Dynamics (Vol. 41). Springer.
Rolls, E. T., Cheng, W., Gilson, M., Gong, W., Deco, G., Lo, C. Y. Z., Yang, A. C., Tsai, S. J., Liu, M. E., Lin, C. P., & Feng, J. (2020). Beyond the disconnectivity hypothesis of schizophrenia. Cerebral Cortex, 30(3), 1213–1233. https://doi.org/10.1093/cercor/bhz161
Schmid, P. J. (2022). Dynamic Mode Decomposition and Its Variants. Annual Review of Fluid Mechanics, 54(1), 225–254. https://doi.org/10.1146/annurev-fluid-030121-015835
Seguin, C., Sporns, O., & Zalesky, A. (2023). Brain network communication: concepts, models and applications. Nature Reviews Neuroscience 2023 24:9, 24(9), 557–574. https://doi.org/10.1038/s41583-023-00718-5
Zalesky, A., & Breakspear, M. (2015). Towards a statistical test for functional connectivity dynamics. NeuroImage, 114, 466–470. https://doi.org/10.1016/j.neuroimage.2015.03.047