Document Detail


Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.
MedLine Citation:
PMID:  20338245     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets.
Authors:
Han Zhang; Xi-Nian Zuo; Shuang-Ye Ma; Yu-Feng Zang; Michael P Milham; Chao-Zhe Zhu
Related Documents :
22162895 - Remote sensing of ambient particles in delhi and its environs: estimation and validation.
18970515 - Metabolic profiling using principal component analysis, discriminant partial least squa...
20408225 - An overview and some new developments in the statistical analysis of pet and fmri data.
18175505 - Dimensional analyses of taxonic data.
18540585 - Characterization of conformational exchange of a histidine side chain: protonation, rot...
24664745 - Mouse models for cone degeneration.
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2010-03-23
Journal Detail:
Title:  NeuroImage     Volume:  51     ISSN:  1095-9572     ISO Abbreviation:  Neuroimage     Publication Date:  2010 Jul 
Date Detail:
Created Date:  2010-05-17     Completed Date:  2010-08-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1414-24     Citation Subset:  IM    
Copyright Information:
Copyright 2010 Elsevier Inc. All rights reserved.
Affiliation:
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Adult
Algorithms
Brain Mapping
Data Interpretation, Statistical
Executive Function / physiology
Female
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging / statistics & numerical data*
Male
Oxygen / blood
Principal Component Analysis
Reproducibility of Results
Rest / physiology
Young Adult
Chemical
Reg. No./Substance:
7782-44-7/Oxygen

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  fMRI adaptation dissociates syntactic complexity dimensions.
Next Document:  High-resolution structural and functional MRI of hippocampal CA3 and dentate gyrus in patients with ...