Predicting behavior through dynamic modes in resting-state fMRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 247; p. 118801
• Main Authors: Ikeda, Shigeyuki, Kawano, Koki, Watanabe, Soichi, Yamashita, Okito, Kawahara, Yoshinobu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2022; Elsevier Limited; Elsevier
• Subjects: Activity patterns; Algorithms; Alzheimer's disease; Behavior; Behavior - physiology; Brain - diagnostic imaging; Brain mapping; Cognition; Cognition - physiology; Connectome - methods; Datasets; Datasets as Topic; Dynamic functional connectivity; Dynamic mode decomposition; Eigenvalues; Female; Functional magnetic resonance imaging; Gender; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging; Male; Neural networks; Neuroimaging; Prediction; Predictive Value of Tests; Rest; Resting-state fMRI; Time series; Young Adult; Resting-state fMRI; Dynamic functional connectivity; Behavior; Dynamic mode decomposition; Prediction
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118801

•Dynamic mode decomposition (DMD) is used to extract dynamic modes from resting fMRI.•Dynamic modes predict individual behavioral differences.•DMD yields better predictive power than independent component analysis.•Dynamic modes share spatial structures with resting-state networks.•DMD efficiently extracts spatiotemporal features from resting-state brain activity. Dynamic properties of resting-state functional connectivity (FC) provide rich information on brain-behavior relationships. Dynamic mode decomposition (DMD) has been used as a method to characterize FC dynamics. However, it remains unclear whether dynamic modes (DMs), spatial-temporal coherent patterns computed by DMD, provide information about individual behavioral differences. This study established a methodological approach to predict individual differences in behavior using DMs. Furthermore, we investigated the contribution of DMs within each of seven specific frequency bands (0–0.1,...,0.6–0.7 Hz) for prediction. To validate our approach, we tested whether each of 59 behavioral measures could be predicted by performing multivariate pattern analysis on a Gram matrix, which was created using subject-specific DMs computed from resting-state functional magnetic resonance imaging (rs-fMRI) data of individuals. DMD successfully predicted behavior and outperformed temporal and spatial independent component analysis, which is the conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that were predicted with significant accuracy in a permutation test were related to cognition. We found that DMs within frequency bands <0.2 Hz primarily contributed to prediction and had spatial structures similar to several common resting-state networks. Our results indicate that DMD is efficient in extracting spatiotemporal features from rs-fMRI data.