fMRI-CPCA

Constrained Principal Component Analysis (CPCA) combines regression analysis and principal component analysis into a unified framework. This method derives images of functional neural networks from singular-value decomposition of BOLD signal time series, and allows derivation of images when the analyzed BOLD signal is constrained to the scans occurring in peristimulus time, using all other scans as baseline.

CPCA provides allows (1) determination of multiple functional networks involved in a task, (2) estimation of the pattern of BOLD changes associated with each functional network over peristimulus time points, (3) quantification of the degree of interaction between these multiple functional networks, and (4) a statistical test of the degree to which experimental manipulations affect each functional network.

fMRI CPCA provides all results in matlab.mat file format, as well as writing images in analyze format for all components, rotated and unrotated.
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Specifications

Category:Principal Component Analysis, Regression
License:Freeware
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Recent Activity

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fMRI_CPCA GUI release

cpca_3.3.5_Mar_11_2010.zip posted by Todd Woodward on Mar 11

fMRI-CPCA Installation & Use release

fMRI_CPCA_Manual_V7.pdf posted by Todd Woodward on Mar 8

fMRI-CPCA Installation & Use release

An_fMRI_CPCA_Tutorial_V3.pdf posted by Todd Woodward on Mar 8

fMRI-CPCA Example Data release

example_data_Multiple_Subject_Runs.zip posted by Todd Woodward on Feb 19

fMRI-CPCA Example Data release

Tutorial_Data_Set.zip posted by Todd Woodward on Feb 18

fMRI-CPCA Example Data release

example_data_Multiple_Subjects.zip posted by Todd Woodward on Feb 17

fMRI-CPCA Example Data release

example_data_Single_Subject.zip posted by Todd Woodward on Feb 17

Patches tracker

Fix written result files datatype. posted by Adam Riggall on Apr 14

Patches tracker

Add free memory support for OS X... posted by Adam Riggall on Apr 14

fMRI-CPCA Literature release

woodward_func_connec_wm.pdf posted by Todd Woodward on Mar 12, 2009