Open AccessCluster analysis of activity‐time series in motor learning
Author(s) -
Balslev Daniela,
Nielsen Finn Å.,
Frutiger Sally A.,
Sidtis John J.,
Christiansen Torben B.,
Svarer Claus,
Strother Stephen C.,
Rottenberg David A.,
Hansen Lars K.,
Paulson Olaf B.,
Law I.
Publication year - 2002
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.10015
Subject(s) - voxel , cluster analysis , smoothing , artificial intelligence , cluster (spacecraft) , neuroimaging , artifact (error) , computer science , pattern recognition (psychology) , psychology , focus (optics) , machine learning , neuroscience , computer vision , physics , optics , programming language
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.