Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA.

@article{Laney2015CapturingSV,
  title={Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA.},
  author={Jonathan Laney and Kelly P Westlake and Sai Ma and Elizabeth J Woytowicz and Vince D. Calhoun and T{\"u}lay Adalı},
  journal={Journal of neuroscience methods},
  year={2015},
  volume={247},
  pages={32-40}
}
BACKGROUND Recent studies using simulated functional magnetic resonance imaging (fMRI) data show that independent vector analysis (IVA) is a superior solution for capturing spatial subject variability when compared with the widely used group independent component analysis (GICA). Retaining such variability is of fundamental importance for identifying spatially localized group differences in intrinsic brain networks. NEW METHODS Few studies on capturing subject variability and order selection… CONTINUE READING

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