Dataset Information

Temporally and spatially constrained ICA of fMRI data analysis.

ABSTRACT: Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to reduce the impact of the accuracy of prior information on CICA, we proposed a temporally and spatially constrained ICA (TSCICA) method that incorporated two types of prior information, both temporal and spatial, as constraints in the ICA. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects who performed a movement task. Additionally, the performance of TSCICA was compared with the ICA method, the temporally CICA (TCICA) method and the spatially CICA (SCICA) method. The results from the simulation and from the real fMRI data demonstrated that TSCICA outperformed TCICA, SCICA and ICA in terms of robustness to noise. Moreover, the TSCICA method displayed better robustness to prior temporal/spatial information than the TCICA/SCICA method.

SUBMITTER: Wang Z

PROVIDER: S-EPMC3984144 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Temporally and spatially constrained ICA of fMRI data analysis.

Wang Zhi Z Xia Maogeng M Jin Zhen Z Yao Li L Long Zhiying Z

PloS one 20140411 4

Constrained independent component analysis (CICA) is capable of eliminating the order ambiguity that is found in the standard ICA and extracting the desired independent components by incorporating prior information into the ICA contrast function. However, the current CICA method produces constraints that are based on only one type of prior information (temporal/spatial), which may increase the dependency of CICA on the accuracy of the prior information. To improve the robustness of CICA and to r ...[more]

PMID: 24727944

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Project description:Chromatin containing the histone variant CENP-A (CEN chromatin) exists as an essential domain at every centromere and heritably marks the location of kinetochore assembly. The size of the CEN chromatin domain on alpha satellite DNA in humans has been shown to vary according to underlying array size. However, the average amount of CENP-A reported at human centromeres is largely consistent, implying the genomic extent of CENP-A chromatin domains more likely reflects variations in the number of CENP-A subdomains and/or the density of CENP-A nucleosomes within individual subdomains. Defining the organizational and spatial properties of CEN chromatin would provide insight into centromere inheritance via CENP-A loading in G1 and the dynamics of its distribution between mother and daughter strands during replication.Using a multi-color protein strategy to detect distinct pools of CENP-A over several cell cycles, we show that nascent CENP-A is equally distributed to sister centromeres. CENP-A distribution is independent of previous or subsequent cell cycles in that centromeres showing disproportionately distributed CENP-A in one cycle can equally divide CENP-A nucleosomes in the next cycle. Furthermore, we show using extended chromatin fibers that maintenance of the CENP-A chromatin domain is achieved by a cycle-specific oscillating pattern of new CENP-A nucleosomes next to existing CENP-A nucleosomes over multiple cell cycles. Finally, we demonstrate that the size of the CENP-A domain does not change throughout the cell cycle and is spatially fixed to a similar location within a given alpha satellite DNA array.We demonstrate that most human chromosomes share similar patterns of CENP-A loading and distribution and that centromere inheritance is achieved through specific placement of new CENP-A near existing CENP-A as assembly occurs each cell cycle. The loading pattern fixes the location and size of the CENP-A domain on individual chromosomes. These results suggest that spatial and temporal dynamics of CENP-A are important for maintaining centromere identity and genome stability.

Dataset Information

Temporally and spatially constrained ICA of fMRI data analysis.

Publications

Temporally and spatially constrained ICA of fMRI data analysis.

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