Removing the effects of task-related motion using independent-component analysis.
Task-related motion is a major source of noise in functional magnetic-resonance imaging (fMRI) time series. The motion effect usually persists even after perfect spatial realignment is achieved. Here, we propose a new method to remove a certain type of task-related motion effect that persists after realignment. The procedure consists of the following: the decomposition of the realigned time-series data into spatially-independent components using independent-component analysis (ICA); the automatic classification and rejection of the ICs of the task-related residual motion effects; and finally, a reconstruction without them. To classify the ICs, we utilized the associated task-related changes in signal intensity and variance. The effectiveness of the method was verified using an fMRI experiment that explicitly included head motion as a main effect. The results indicate that our ICA-based method removed the task-related motion effects more effectively than the conventional voxel-wise regression-based method.
Kochiyama
Takanori
T
Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan.
Morita
Tomoyo
T
Okada
Tomohisa
T
Yonekura
Yoshiharu
Y
Matsumura
Michikazu
M
Sadato
Norihiro
N
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D019416:Head Movements / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D009043:Motor Activity / Q000502:physiology*; D025341:Principal Component Analysis; D012372:ROC Curve