Investigations into resting-state connectivity using independent component analysis
Tóm tắt
Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.
Từ khóa
Tài liệu tham khảo
Beckmann C, 2001, Investigating the intrinsic dimensionality of fMRI data for ICA, Seventh International Conference on Functional Mapping of the Human Brain, Brighton, 10–14 June 2001. NeuroImage, 13, S76
Beckmann C, 2003, Gaussian/Gamma mixture modelling of ICA/GLM spatial maps, Ninth International Conference on Functional Mapping of the Human Brain, New York, 18–22 June 2003. NeuroImage, 19, S985
DeLuca M, 2002, Low frequency signals in fMRI—“resting state networks” and the “intensity normalisation problem”, Proceedings of the International Society of Magnetic Resonance in Medicine, Sendai, Japan, 2–6 June, 2002. NeuroImage, 16, S480
DeLuca M, 2002, Eighth International Conference on Functional Mapping of the Human Brain
DeLuca M. Beckmann C. Clare S. Matthews P. De Stefano N. Smith S. Submitted. Investigations of resting state networks in fMRI. NeuroImage.
Dempster A.P, 1977, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. R. Stat. Soc. Ser. B, 39, 1
Goldman R, 2003, Tomographic distribution of resting alpha rhythm sources revealed by independent component analysis, Ninth International Conference on Functional Mapping of the Human Brain, New York, 18–22 June 2003. NeuroImage, 19, S412
Johnstone I. 2000 On the distribution of the largest principal component. Technical report Department of Statistics Stanford University.
Minka T. 2000 Automatic choice of dimensionality for PCA. Technical Report 514 MIT Media Lab.
Petersen K, 2000, Second International Symposium on Independent Component Analysis and Blind Signal Separation
Roberts S& Everson R. 2001 Cambridge:Cambridge University Press.