The main challenge that we intend to address involves the charting of a
functional atlas from one or more functional Magnetic Resonance Imaging
(fMRI) datasets. Our hypothesis will be that the fMRI signal can be
described by a small number of parameters, in comparison to the large
number of degrees of freedom (temporal and spatial) of the original
dataset.
We take advantage of the implicit low dimensionality of the dataset to
construct, in an unsupervised way, a new parametrization of the dataset.
The new parametrization creates meaningful clusters allowing the
separation of the dataset into: (1) activated voxels, (2) artefactual
signals, and (3) a clutter formed by the background time series.
We have conducted several experiments with synthetic and in-vivo data that
demonstrate the performance of our approach.
webpage: http://ece.colorado.edu/~fmeyer