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Initial K-Means Clustering :

Preprocessing:. Zeroing the mean of each trial in a run. Initial K-Means Clustering :. Time courses normalized to vectors of unit length and clustered Clusters selected for further analysis by visual inspection. Finite Impulse Response Filtering :.

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Initial K-Means Clustering :

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  1. Preprocessing: • Zeroing the mean of each trial in a run Initial K-Means Clustering: • Time courses normalized to vectors of unit length and clustered • Clusters selected for further analysis by visual inspection Finite Impulse Response Filtering: • Selected voxels deconvolved with paradigm functions to get the FIR’s of each • Paradigm functions used: motor preparation & execution Meta-K-Means Clustering: • Normalized to unit vectors and K-means clustered as previously

  2. Conclusions: • The voxels in the motor areas clearly showed differences in activation in terms of temporal relation to the planning and execution phases of a motor task. • FIR filtering followed by K-means clustering on the coefficients, provides a good tool not only to separate voxels of brain tissue and veins with different temporal activation patterns in single-trial experiments, but also to visualize the BOLD response waveform. This project was supported by NIH (MH57180 and RR08079).

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