title: Cluster analysis of activity-time series in motor learning.
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.
authors.affiliation: Neurobiology Research Unit, N 9201, Copenhagen University Hospital, Rigshospitalet, 9 Blegdamsvej, 2100 Copenhagen, Denmark. daniela@nru.dk
authors.forename: Daniela
authors.initials: D
authors.lastname: Balslev
authors.forename: Finn A
authors.initials: FA
authors.lastname: Nielsen
authors.forename: Sally A
authors.initials: SA
authors.lastname: Frutiger
authors.forename: John J
authors.initials: JJ
authors.lastname: Sidtis
authors.forename: Torben B
authors.initials: TB
authors.lastname: Christiansen
authors.forename: Claus
authors.initials: C
authors.lastname: Svarer
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Strother
authors.forename: David A
authors.initials: DA
authors.lastname: Rottenberg
authors.forename: Lars K
authors.initials: LK
authors.lastname: Hansen
authors.forename: Olaf B
authors.initials: OB
authors.lastname: Paulson
authors.forename: I
authors.initials: I
authors.lastname: Law
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D016011:Normal Distribution; D011597:Psychomotor Performance / Q000502:physiology*; D018709:Statistics, Nonparametric; D013997:Time Factors; D014055:Tomography, Emission-Computed / Q000379:methods / Q000706:statistics & numerical data