Impact of differences between real and predicted time series on GLM fMRI analysis
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| Year of publication | 2006 |
| Type | Conference abstract |
| MU Faculty or unit | |
| Citation | |
| Description | In functional magnetic resonance imaging (fMRI) detection of activation is often realized using massively univariate statistical methods. The measured signal is modeled using convolution of stimulus time-course and hemodynamic response function (HRF). For accurate fitting the data to the model it is necessary to know both HRF and stimulus time-course. The aim of this work is to find how much the results are depended on inaccurate knowledge of stimulus time-course. |
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