An improvement of independent component analysis with projection method applied to multi-task fMRI data.
Independent Component Analysis with projection (ICAp) method proposed by Long et al. Hum. Brain Mapp. 30 (2009) 417-431, can solve the interaction among task-related components of multi-task functional magnetic resonance imaging (fMRI) data. However, the departure of the ideal homodynamic response function (HRF) for projection from the true HRF may worse the ICAp results. In order to improve the performance of ICAp, the deconvolved ICAp (DICAp) method is proposed. Both the simulated and real fMRI experiments demonstrate that DICAp can separate more accurate time course corresponding to each task-related components and is more powerful to detect regions activated by each task only than ICAp.
State Key Lab of Cognitive Neuroscience and Learning, School of Information Science, Beijing Normal University, and Laboratory of Magnetic Resonance Imaging, Beijing 306 Hospital, Beijing 100875, China.
Zhiying
Z
Long
Rui
R
Li
Mingqi
M
Hui
Zhen
Z
Jin
Li
L
Yao
D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D011859:Radiography