Profiling Dynamic Spatial States of Nonlinear fMRI Networks and Associations with Schizophrenia
Poster No:
1414
Submission Type:
Abstract Submission
Authors:
Spencer Kinsey1, Vince Calhoun1, Armin Iraji1
Institutions:
1Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA
First Author:
Spencer Kinsey
Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Co-Author(s):
Vince Calhoun
Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Center for Translational Research in Neuroimaging and Data Science (TReNDS)
Atlanta, GA
Introduction:
Functional connectivity studies have typically been designed to capture networks that reflect linear relationships between spatially fixed nodes (Calhoun et al, 2009; Friston, 2011). However, such approaches miss explicitly nonlinear (ENL) relationships that may shed light on functional brain organization and psychiatric illnesses (Kinsey et al., 2024), and canonical networks exhibit spatial variation over time (Iraji et al., 2019; Iraji et al., 2022). Therefore, rigorously characterizing the dynamic spatial profiles of ENL network counterparts may enhance our understanding of brain (dys)function. Here, we build on our previous research framework (Kinsey et al., 2024) by advancing a method to estimate ENL whole-brain functional connectivity (ENL-wFC) as the fMRI distance correlation (Székely et al., 2007) information not explained by distance correlation values obtained under the assumption of standard normality. Using this approach, we constructed spatially dynamic ENL network profiles with independent component analysis (ICA) and investigated their associations with schizophrenia (SZ).
Methods:
We analyzed resting-state fMRI data from 193 individuals with SZ and 315 healthy controls (HC) (n = 508). Information about demographics, scan protocols, and data preprocessing steps can be found in Kinsey et al. (2024). We first calculated the linear whole-brain functional connectivity for each subject as covariance (Cov) between Z-scored voxel time courses (Fig. 1), which is equal to the pairwise Pearson correlation. We then calculated the pairwise distance correlation (dCor-observed) (Székely et al., 2007). We note that distance correlation is sensitive to both linear and nonlinear dependence. To remove linear dependence information from dCor-observed, we transformed Pearson correlation information into distance correlation under an assumption of standard normality (dCor-null) using Theorem 7 (ii) as detailed in Székely et al. (2007). We then used a linear regression-based approach to remove the dCor-observed information explained by dCor-null and obtain ENL-wFC.
Using group-level ICA, we estimated 20 components from Pearson correlation and ENL-wFC respectively, and we identified networks based on rigorous criteria (Kinsey et al., 2024). To obtain subject-specific dynamic spatial maps, we reconstructed networks within 30 repetition time (TR) windows across scans (Iraji et al., 2019). To identify salient network spatial states, we clustered maps using k-means clustering (k = 4). We then analyzed differences in the number of state transitions between corresponding networks (spatial correlation > .80 between counterparts) using permutation tests with 5000 random permutations. Finally, we analyzed associations between SZ diagnosis and state fraction time, mean dwell time, and number of transitions using a general linear model while controlling for confounds including age, sex, race, site, and motion.
Using group-level ICA, we estimated 20 components from Pearson correlation and ENL-wFC respectively, and we identified networks based on rigorous criteria (Kinsey et al., 2024). To obtain subject-specific dynamic spatial maps, we reconstructed networks within 30 repetition time (TR) windows across scans (Iraji et al., 2019). To identify salient network spatial states, we clustered maps using k-means clustering (k = 4). We then analyzed differences in the number of state transitions between corresponding networks (spatial correlation > .80 between counterparts) using permutation tests with 5000 random permutations. Finally, we analyzed associations between SZ diagnosis and state fraction time, mean dwell time, and number of transitions using a general linear model while controlling for confounds including age, sex, race, site, and motion.
Results:
We identified 13 corresponding networks between datasets along with 4 networks (3 ENL, 1 linear) that did not meet our correspondence threshold. ENL networks exhibit state patterns not reflected in linear counterparts. For example, the ENL anterior default mode (aDM) network exhibits transient integration with posterior default mode regions (Fig. 2a). Significantly higher transition dynamism is observed for ENL vs. linear networks (p < .01 for all comparisons; see Fig. 2b & 2d for examples). Furthermore, the state one fraction rate (p < .0001) and mean dwell time (p < .001) of a unique ENL network previously reported in Kinsey et al. (2024) are negatively associated with SZ diagnosis.
Conclusions:
Our research reveals structured fMRI connectivity information not explained by bivariate gaussianity. ENL networks extracted from these patterns exhibit higher spatial dynamism than linear counterparts and hidden associations with SZ, illuminating potential roles in brain (dys)function.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Task-Independent and Resting-State Analysis
Keywords:
FUNCTIONAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Friston K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1(1), 13–36. https://doi.org/10.1089/brain.2011.0008
Iraji, A. et al. (2019). The spatial chronnectome reveals a dynamic interplay between functional segregation and integration. Human Brain Mapping, 40(10), 3058–3077. https://doi.org/10.1002/hbm.24580
Iraji, A. et al. (2022). Multi-spatial-scale dynamic interactions between functional sources reveal sex-specific changes in schizophrenia. Network Neuroscience (Cambridge, Mass.), 6(2), 357–381. https://doi.org/10.1162/netn_a_00196
Kinsey, S. et al. (2024). Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls. Nature Mental Health, 2(12), 1464–1475. https://doi.org/10.1038/s44220-024-00341-y
Székely, G. J. et al. (2007). Measuring and Testing Dependence by Correlation of Distances. The Annals of Statistics, 35(6), 2769–2794. http://www.jstor.org/stable/25464608