On clustering fMRI time series
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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
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TY - JOUR
T1 - On clustering fMRI time series
AU - Goutte, C
AU - Toft, P
AU - Rostrup, E
AU - Nielsen, F
AU - Hansen, L K
N1 - Copyright 1999 Academic Press.
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