Informed inter-brain coupling improves predictions of mental health outcomes
Poster No:
1393
Submission Type:
Abstract Submission
Authors:
Haily Merritt1, Rick Betzel2, Giovanni Petri3
Institutions:
1Indiana University, Bloomington, IN, 2University of Minnesota, Minneapolis, MN, 3Northeastern University London, London, London
First Author:
Co-Author(s):
Introduction:
Recent years have seen increased emphasis on studying how and in what context brain network organization varies across individuals. An effective tool for investigating variability in an ensemble of networks is multilayer modeling, where each network is a layer and layers are coupled to one another for analysis. Traditionally, coupling between layers is often defined uniformly by a single parameter, but this approach risks mischaracterizing variability that is consequential for brain function. Here, we leverage the richness of the Human Connectome Project dataset to compare how uniform versus informed coupling predicts outcomes across a suite of measures.
Methods:
For both models, layer l is represented by subject l's functional connectivity brain network, as indexed by the Pearson correlation of the time series of the 100 region Schaefer parcellation (see Figure 1a). In the uniform model, all-to-all coupling of layers (i.e., inter-brain coupling) is defined by a universal parameter value ω; we sweep across 9 log-spaced values between 0.0010 − 1 (see Figure 1b). To define inter-brain coupling in the informed model, we first compute the correlation between all subjects across 9 measures of the social environment (see Figure 1c). To evaluate the impact of social environment similarity, we then interpolate 10 distributions between a δ-distribution at 0 and the empirical distribution of social environment correlations using a mixture model, where values of the mixture parameter α are log-spaced (see Figure 1d). Informed coupling values are taken from one distribution per analysis, such that a parameter sweep of α is akin to tuning how much social context similarity informs interlayer coupling (see Figure 1b). For both the uniform and informed models, we perform community detection 100 times using a uniform null model, compute consensus communities, and use these community affiliations to calculate flexibility, or how much a node switches its community assignment between layers. We also perform a sweep across the community resolution parameter but here report only γ = 0.4. We use flexibility and community assignments to compare how these models fare in predicting outcomes across 37 measures of cognition, emotion, life satisfaction, and behavior.
Results:
At a focal point in parameter space, community structure of the uniform coupling model is dominated by two communities (≈ 75% of nodes across all layers assigned to one of these two communities), roughly representing sensorimotor and association cortices (see Figure 1e). The flexibility of nodes across canonical brain systems varies, such that Temporoparietal nodes tend to be more flexible and Somatomotor, Visual, and Dorsal Attention nodes are less flexible (see Figure 1f). We found moderate to no associations between the community organization of the uniform model and scores on the outcome measures, but there was a general trend such that the more similar the partition of layer l was to layer k, the more similarly subjects k and l scored on the outcome measures. (see Figure 1g). As inter-brain coupling is increasingly informed by social context similarity, we see greater diversity in community assignments than with uniform coupling, with somatosensory nodes split across Communities 1 and 3 (see Figure 1h). While the Control system is still quite flexible, SalVentAttn nodes are more flexible and DorsAttn nodes are less flexible (see Figure 1i). Predictions are comparable to the uniform model for many outcome measures, but there is an improvement on predictions of mental health (see Figure 1j).
Conclusions:
Informed inter-brain coupling in a multilayer model provides useful context for understanding variability in brain network organization. This context serves to improve predictions on associations with mental health outcomes, which has consequences for the identification of brain-network based biomarkers as well as provide insights on the functional brain mechanisms linking environments and health outcomes
Emotion, Motivation and Social Neuroscience:
Social Neuroscience Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Methods Development
Keywords:
Data analysis
FUNCTIONAL MRI
Multivariate
Social Interactions
Other - network science
1|2Indicates the priority used for review
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[3] Merritt, H., Faskowitz, J., Gonzalez, M. Z., & Betzel, R. F. (2024). Stability and variation of brain-behavior correlation patterns across measures of social support. Imaging Neuroscience, 2, 1-18.