Functional neuroimaging independent component analysis

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Functional neuroimaging independent component analysis
Category: Functional neuroimaging independent component analysis

Independent component analysis
Functional neuroimaging
Neuroimaging analysis

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Functional neuroimaging independent component analysis is independent component analysis (ICA) used in functional neuroimaging, especially functional magnetic resonance imaging (fMRI). Two modes are commonly considered:

  1. Temporal ICA
  2. Spatial ICA

Other methods are:

  1. Convolutive ICA.[1]
  2. Complex ICA in the frequency domain.[2]


[edit] Tools

  1. AnalyzeFMRI in R[3]
  2. Brede Toolbox
  3. FMRLAB[4]
  4. GIFT[5]
  5. Lyngby Toolbox[6]
  6. MELODIC from FSL

[edit] Papers

[edit] Reviews

  1. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data
  2. ICA of functional MRI data: an overview
  3. Independent component analysis of functional MRI: what is signal and what is noise?
  4. Unmixing fMRI with independent component analysis

[edit] Original articles

  1. Analysis of fMRI data by blind separation into independent spatial components
  2. Comparison of multi-subject ICA methods for analysis of fMRI data
  3. Quality map thresholding for de-noising of complex valued fMRI data and its application to ICA of fMRI
  4. Spatial independent component analysis for multi-task functional MRI data processing

[edit] External links

  1. Finn Årup Nielsen, Bibliography on Independent Component Analysis in Functional Neuroimaging.

[edit] References

  1. ICA if fMRI based on a convolutive mixture model
  2. Jörn Anemüller, Jeng-Ren Duann, Terrence J. Sejnowski, Scott Makeig(2004). "Unraveling spatio-temporal dynamics in fMRI recordings using complex ICA". Pages 1103-1110 in Independent Component Analysis and Blind Signal Separation.
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