Predicting behavior through dynamic modes in resting-state fMRI data

Abstract

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 confirmed 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. The prediction was successful, and DMD outperformed temporal independent component analysis, a conventional data decomposition method for extracting spatial activity patterns. Most of the behavioral measures that showed significant prediction accuracies in a permutation test were cognitive-behavioral measures. Our results suggested that DMs within frequency bands <0.2 Hz primarily contributed to prediction. In addition, we found that DMs <0.2 Hz had spatial structures similar to several common resting-state networks. We demonstrated the effectiveness of DMs, indicating that DMD is a key approach for extracting spatiotemporal features from rs-fMRI data.