On clustering fMRI time series

Cyril Goutte, Peter Aundal Toft, E. Rostrup, Finn Årup Nielsen, Lars Kai Hansen

    Research output: Contribution to journalJournal articlepeer-review


    Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.
    Original languageEnglish
    Issue number3
    Pages (from-to)298-310
    Publication statusPublished - 1999


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