Abstract
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 i 0 is activated is based not solely on the value of the fMRI signal at i 0, but rather on the comparison of all time series x i (t) in a small neighborhood \(W_{i_{\rm 0}}\) around i 0. 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.
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Meyer, F.G., Chinrungrueng, J. (2003). Analysis of Event-Related fMRI Data Using Best Clustering Bases. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_52
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DOI: https://doi.org/10.1007/978-3-540-45087-0_52
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