On clustering fMRI time series

Forskning

On clustering fMRI time series

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

On clustering fMRI time series. / Goutte, C; Toft, P; Rostrup, E; Nielsen, F; Hansen, L K.

In: NeuroImage, Vol. 9, No. 3, 1999, p. 298-310.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Goutte, C, Toft, P, Rostrup, E, Nielsen, F & Hansen, LK 1999, 'On clustering fMRI time series', NeuroImage, vol. 9, no. 3, pp. 298-310.

APA

Goutte, C., Toft, P., Rostrup, E., Nielsen, F., & Hansen, L. K. (1999). On clustering fMRI time series. NeuroImage, 9(3), 298-310.

Vancouver

Goutte C, Toft P, Rostrup E, Nielsen F, Hansen LK. On clustering fMRI time series. NeuroImage. 1999;9(3):298-310.

Author

Goutte, C ; Toft, P ; Rostrup, E ; Nielsen, F ; Hansen, L K. / On clustering fMRI time series. In: NeuroImage. 1999 ; Vol. 9, No. 3. pp. 298-310.

Bibtex

@article{57ce2b2f07934962803263146f23c51a,

title = "On clustering fMRI time series",

abstract = "Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.",

author = "C Goutte and P Toft and E Rostrup and F Nielsen and Hansen, {L K}",

year = "1999",

language = "English",

volume = "9",

pages = "298--310",

journal = "NeuroImage",

issn = "1053-8119",

publisher = "Elsevier",

number = "3",

}

RIS

TY - JOUR

T1 - On clustering fMRI time series

AU - Goutte, C

AU - Toft, P

AU - Rostrup, E

AU - Nielsen, F

AU - Hansen, L K

PY - 1999

Y1 - 1999

N2 - Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.

AB - Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus.

M3 - Journal article

VL - 9

SP - 298

EP - 310

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 3

ER -

ID: 34158910