Toward a neurometric foundation for probabilistic independent component analysis of fMRI data.

@article{Poppe2013TowardAN,
  title={Toward a neurometric foundation for probabilistic independent component analysis of fMRI data.},
  author={Andrew B Poppe and Krista Wisner and G. Atluri and Kelvin O. Lim and Vipin Kumar and Angus W. MacDonald},
  journal={Cognitive, affective & behavioral neuroscience},
  year={2013},
  volume={13 3},
  pages={641-59}
}
Improved fMRI data analysis methods hold promise for breakthroughs in cognitive and affective neuroscience. Group probabilistic independent component analysis (pICA), such as that implemented by MELODIC (Beckmann & Smith IEEE Transactions on Medical Imaging 23:137-152, 2004), is one popular technique that typifies this development. Recently pICA has been proposed to be a reliable method for studying connectivity networks (Zuo et al. NeuroImage 49:2163-2177, 2010); however, there is no "standard… CONTINUE READING

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