Cluster analysis of activity-time series in motor learning

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

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)135-145
Number of pages11
JournalHuman Brain Mapping
Volume15
Issue number3
DOIs
StatePublished - Mar 14 2002

Keywords

  • Cross-validation
  • Functional neuroimaging
  • Generalization error
  • Multivariate analysis
  • Positron emission tomography

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