Cluster analysis of activity-time series in motor learning.

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

Cluster analysis of activity-time series in motor learning.

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

Department of Clinical Medicine

28 Citations (Scopus)

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 language	English
Journal	Human Brain Mapping
Volume	48
Issue number	2
Pages (from-to)	351-361
ISSN	1065-9471
Publication status	Published - 2002

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Balslev, D., Nielsen, F. Å., Futiger, S. A., Sidtis, J. J., Christiansen, T. B., Svarer, C., Strother, S. C., Rottenberg, D. A., Hansen, L. K., Paulson, O. B., & Law, I. (2002). Cluster analysis of activity-time series in motor learning. Human Brain Mapping, 48(2), 351-361. http://www.ncbi.nlm.nih.gov//entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11835604

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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",

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AU - Balslev, Daniela

AU - Nielsen, Finn Å

AU - Futiger, Sally A

AU - Sidtis, John J

AU - Christiansen, Torben B

AU - Svarer, Claus

AU - Strother, Stephen C.

AU - Rottenberg, David A

AU - Hansen, Lars K

AU - Paulson, Olaf B.

AU - Law, I

PY - 2002

Y1 - 2002

N2 - 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

AB - 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

M3 - Journal article

SN - 1065-9471

VL - 48

SP - 351

EP - 361

JO - Human Brain Mapping

JF - Human Brain Mapping

IS - 2

ER -

Cluster analysis of activity-time series in motor learning.

Abstract

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