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Abstract #1347

Connectome caricatures: large-amplitude co-activation patterns in resting-state fMRI hide sources of individual differences

Raimundo Rodriguez1, Stephanie Noble2, Chris Camp1, and Dustin Scheinost3
1Interdepartmental Neuroscience Program, Yale University, New Haven, CT, United States, 2Department of Psychology, Northeastern University, Boston, MA, United States, 3Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States

Synopsis

Keywords: Functional Connectivity, fMRI (resting state), low-dimensional, reliability, discriminability, connectome-based predictive modeling, CPM, networks

Motivation: Task-like co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity. However, little research has characterized the remaining signal.

Goal(s): We aimed to characterize the hidden resting-state signal that exists beyond the dominating co-activation patterns and assess its merit for studying individual differences.

Approach: We used task-based fMRI data to construct a task-relevant co-activation pattern manifold. By projecting resting-state time series data orthogonally to this manifold, we created Caricatured connectomes.

Results: Like caricatures, these connectomes emphasized individual differences while reducing between-individual similarity. They also represented individual differences in behavior, often to a greater degree than Standard connectomes.

Impact: A distinct signal carrying information about individual differences exists beyond the dominating co-activations that drive resting-state functional connectivity. This signal may better characterize the brain’s intrinsic functional architecture and can be used to evaluate novel sources of individual differences.

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Keywords