Spatiotemporal Independent Component Analysis of Event-Related fMRI Data Using Skewed Probability Density Functions

By James V. Stone, John Porrill, N. Porter, Iain D. Wilkinson. 2002

195 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrochemistry, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Christian F. Beckmann
2. Stephen M. Smith
3. Bilwaj Gaonkar
4. Russell T. Shinohara
5. Soo-Young Lee
6. Christos Davatzikos
7. George S. Alexopoulos
8. Kelvin O. Lim

In Electrochemistry

1. Soo-Young Lee
2. Seungjin Choi

In Signal Processing

1. Christian F. Beckmann
2. Stephen M. Smith
3. Soo-Young Lee
4. Mark J. Schnitzer
5. Eran A. Mukamel
6. A. Nimmerjahn
7. Matthew J. Hoptman
8. Kelvin O. Lim

Citations in the corpus

• Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “It remains one of the most prolific methods used for fMRI data analysis Xu et al. (2009) McKeown and Sejnowski (1998) Smith et al. (2004) Svenséen et al. (2002) Stone et al. (2002) Perlbarg et al. (2007).”
• Independent Component Analysis (2005, unknown): “Whereas tICA assumes independence over time but not space, and sICA assumes independence over space but not time, stICA assumes independence over time and space (Stone et al., 2002).”
• Probabilistic independent component analysis for functional magnetic resonance imaging (2004, result): “This is an effect quite different to the assumptions that lead into the investigation of “spatiotemporal independent component analysis” [45].”
• Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “A variety of methods can be used to assess the improvement in sensitivity and/or specificity of fMRI data analysis after denoising (Biswal et al., 1996; Liu et al., 2001; Stone et al., 2002; Thomas et al., 2002; Kochiyama et al., 2005; McKeown et al., 2005; Gretton et al., 2006).”
• Blind Source Separation and Independent Component Analysis: A Review (2005, methodology): “Spatial ICA or spatio-temporal ICA were shown to be useful in fMRI image analysis [133] and gene expression data analysis [103, 86].”
• Automated analysis of cellular signals from large-scale calcium imaging data. (2009, methodology): “We did this using spatiotemporal ICA, which optimizes a linear combination of spatial and temporal skewness (Stone et al., 2002).”
• Computer Methods and Programs in Biomedicine (2019, methodology): “Lastly, we performed the spatiotemporal independent component analysis (SIMA [10,27] because this algorithm combines both spatial and temporal information to determine the cell’s locations.”
• Blind Source Separation and Independent Component Analysis : A Review (2004, methodology): “Spatial ICA or spatio-temporal ICA were shown to be useful in fMRI image analysis [133] and gene expression data analysis [103, 86].”