Predicting behavior through dynamic modes in resting-state fMRI data
Shigeyuki Ikeda, Koki Kawano, Soichi Watanabe, Okito Yamashita, Yoshinobu Kawahara
This study uses dynamic mode decomposition to predict individual behavioral differences from resting-state fMRI data, finding that low-frequency dynamic modes are most effective.
NeuroImage · Dec 10, 2021Peer-reviewedNeuroscience