Analysis of event-related fMRI data using best clustering bases.
We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i)(t), indexed by the position i of a voxel inside the brain. The decision that a voxel i0 is activated is based not solely on the value of the fMRI signal at i0, but rather on the comparison of all time series x(i)(t) in a small neighborhood Wi0 around i0. We construct basis functions on which the projection of the fMRI data reveals the organization of the time-series x(i)(t) into "activated", and "non-activated" clusters. These "clustering basis functions" are selected from large libraries of wavelet packets according to their ability to separate the fMRI time-series into the activated cluster and a non activated cluster. This principle exploits the intrinsic spatial correlation that is present in the data.
Meyer
François G
FG
Department of Electrical Engineering, University of Colorado at Boulder, Boulder, CO 80309-0425, USA. francois.meyer@colorado.edu
Chinrungrueng
Jatuporn
J
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique