An improvement of independent component analysis with projection method applied to multi-task fMRI data

Zhiying Long; Rui Li; Mingqi Hui; Zhen Jin; Li Yao

doi:10.1016/j.compbiomed.2012.11.013

An improvement of independent component analysis with projection method applied to multi-task fMRI data

Comput Biol Med. 2013 Mar;43(3):200-10. doi: 10.1016/j.compbiomed.2012.11.013. Epub 2013 Jan 21.

Authors

Zhiying Long¹, Rui Li, Mingqi Hui, Zhen Jin, Li Yao

Affiliation

¹ 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.

PMID: 23347509
DOI: 10.1016/j.compbiomed.2012.11.013

Abstract

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.

Publication types

Clinical Trial
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Brain / diagnostic imaging*
Brain Mapping / methods*
Female
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Male
Models, Theoretical*
Radiography