2009
DOI: 10.1002/hbm.20813
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Functional segmentation of the brain cortex using high model order group PICA

Abstract: Baseline activity of resting state brain networks (RSN) in a resting subject has become one of the fastest growing research topics in neuroimaging. It has been shown that up to 12 RSNs can be differentiated using an independent component analysis (ICA) of the blood oxygen level dependent (BOLD) resting state data. In this study, we investigate how many RSN signal sources can be separated from the entire brain cortex using high dimension ICA analysis from a group dataset. Group data from 55 subjects was analyze… Show more

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Cited by 347 publications
(385 citation statements)
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“…In the current experiment, the spatial resolution of the 3T EPI images combined with standard preprocessing and decomposition of the data with ICA does not allow for a robust extraction of components as small as the STN (Kiviniemi et al, 2009). Therefore, we used probability maps of the left and right STN (B.U.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the current experiment, the spatial resolution of the 3T EPI images combined with standard preprocessing and decomposition of the data with ICA does not allow for a robust extraction of components as small as the STN (Kiviniemi et al, 2009). Therefore, we used probability maps of the left and right STN (B.U.…”
Section: Discussionmentioning
confidence: 99%
“…Then, group-level aggregate data were generated by concatenating and reducing individual principal components in a second PCA step. Infomax ICA (Bell and Sejnowski, 1995) was performed in this set with a model order of 60 components (Kiviniemi et al, 2009). To estimate robust components we used ICASSO (Himberg et al, 2004), i.e., the decomposition was performed 100 times with random initial conditions, and identified centroids with a canonical correlation-based clustering.…”
Section: Methodsmentioning
confidence: 99%
“…The group‐level dimensionality for PCA and ICA decompositions (i.e., 61) was set to the minimum order to retain 100% nonzero Eigen values during individual PCA reduction, which is close estimate of the true degree of freedom in the data. The major purpose underlying this procedure was to use a high‐order decomposition [Kiviniemi et al, 2009] to maximize the observable effects while avoiding possible overfitting errors [Sarela and Vigario, 2003]. …”
Section: Methodsmentioning
confidence: 99%
“…To fractionate the TPJ into more specific components, we used a local independent component analysis (ICA) analysis. ICA is a blind source-separation technique that has previously been used to parcellate fMRI data into spatially overlapping, but maximally independent, spatiotemporal components (28)(29)(30)(31)(32)(33)(34). We previously showed that local ICA can reliably parcellate the TPJ into spatiotemporal components, each functionally connected to distinct, brain-wide networks (32).…”
mentioning
confidence: 99%