ICA applied to functional Magnetic Resonance Imaging (fMRI) data analysis

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fMRI data

fMRI data is a complicated mixture of different sources of variability: cardiac and respiratory pulsations, subtle head movements, task-related activity changes and machine noise. Changes related to the performance of psychomotor tasks may constitute as little as 10-15% of the variance of the Blood Oxygen Level Dependent (BOLD) contrast signal in a 1.5T magnet, so extracting the small task-related changes from the measured signal is difficult.

ICA decomposition of fMRI data

ICA, in the manner applied to ERP and EEG (more here), is inappropriate for fMRI analysis because the number of "channels" (i.e. voxels) greatly exceeds the number of time points in a typical fMRI experiment. In 1997, it was first proposed to look for spatially independent patterns of activity in fMRI data [pdf]. This assumes that the spatial distributions associated with each of the above sources of variability are independent, and that the contributions from each spatial pattern sum linearly to represent the data. The time courses associated with the different spatial patterns can potentially be correlated, allowing for the detection of spatial patterns whose time courses are transiently task-related (TTR) as well as consistently task-related (CTR). The criteria of spatial independence appears to be a powerful way to separate task-related activations from other sources of variability making up the BOLD signal, as explained in Frequently Asked Questions about ICA applied to fMRI data.

Further details have been published in a PNAS paper of ICA applied to fMRI data [pdf] and a Human Brain Mapping paper [pdf].

Toolbox

A Matlab toolbox for fMRI data analysis using Independent Components Analysis, called FMRLAB, is available. Please see the FMRLAB homepage at the Swartz Center for Computational Neuroscience (UCSD) for more information.