Cluster analysis of activity-time series in motor learning

Daniela Balslev, Finn Årup Nielsen, Sally A. Frutiger, John J. Sidtis, Torben Christiansen, Claus Svarer, Stephen C. Strother, David A. Rottenberg, Lars Kai Hansen, Olaf B. Paulson, Ian Law

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    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15:135-145, 2002. © 2002 Wiley-Liss, Inc.
    Original languageEnglish
    JournalHuman Brain Mapping
    Issue number3
    Pages (from-to)135-145
    Publication statusPublished - 2002


    • motor learning
    • positron emission tomography
    • cluster analysis

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