Fully exploratory network ICA (FENICA) on resting-state fMRI data.

@article{Schpf2010FullyEN,
  title={Fully exploratory network ICA (FENICA) on resting-state fMRI data.},
  author={Veronika Sch{\"o}pf and Christian H. Kasess and Rupert Lanzenberger and F Fischmeister and Christian Windischberger and Ewald Moser},
  journal={Journal of neuroscience methods},
  year={2010},
  volume={192 2},
  pages={207-13}
}
Independent component analysis (ICA) is one of the most valuable explorative methods for analyzing resting-state networks (RSNs) in fMRI, representing a data-driven approach that enables decomposition of high-dimensional data into discrete components. Extensions to a group-level suffer from the drawback of evaluating single-subject resting-state components of interest either using a predefined spatial template or via visual inspection. FENICA introduced in the context of group ICA methods is… CONTINUE READING

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