Paper Info
Title
Generalized RAICAR: discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks.
Abstract
Existing spatial independent component analysis (ICA) methods for multi-subject fMRI datasets have mainly focused on detecting common components across subjects, under the assumption that all the subjects in a group share the same (identical) components. However, as a data-driven approach, ICA could potentially serve as an exploratory tool at multi-subject level, and help us uncover inter-subject differences in patterns of connectivity (e.g., find subtypes in patient populations). In this work, we propose a methodology named gRAICAR that exploits the data-driven nature of ICA to allow discovery of sub-groupings of subjects based on reproducibility of their ICA components. This technique allows us not only to find highly reproducible common components across subjects but also to explore (without a priori subject groupings) components that could classify all subjects into sub-groups. gRAICAR generalizes the reproducibility framework previously developed for single subjects (Ranking and averaging independent component analysis by reproducibility—RAICAR—Yang et al., Hum Brain Mapp, 2008) to multiple-subject analysis. For each group-level component, gRAICAR generates its reproducibility matrix and further computes two metrics, inter-subject consistency and intra-subject reliability, to characterize inter-subject variability and reflect contributions from individual subjects. Nonparametric tests are employed to examine the significance of both the inter-subject consistency and the separation of subject groups reflected in the component. Our validations based on simulated and experimental resting-state fMRI datasets demonstrated the advantage of gRAICAR in extracting features reflecting potential subject groupings. It may facilitate discovery of the underlying brain functional networks with substantial potential to inform our understandings of development, neurodegenerative conditions, and psychiatric disorders.
Year
DOI
Venue
2012
10.1016/j.neuroimage.2012.06.060
NeuroImage
Keywords
Field
DocType
Independent component analysis,Reproducibility,Group discovery,Sample homogeneity,Exploratory analysis,Resting state
Data mining,Reproducibility,Computer science,Resting state fMRI,A priori and a posteriori,Cognitive psychology,Artificial intelligence,Pattern recognition,Ranking,Homogeneous,Nonparametric statistics,Independent component analysis,Instrumental and intrinsic value
Journal
Volume
Issue
ISSN
63
1
1053-8119
Citations 
PageRank 
References 
4
0.43
13
Authors
7
Name
Order
Citations
PageRank
Zhi Yang1151.46
Xi-Nian Zuo262530.73
Peipei Wang340.43
Zhihao Li4175.10
Stephen LaConte526528.11
Peter A Bandettini61962151.79
Xiaoping Hu740859.63