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| Content Provider | PubMed Central |
|---|---|
| Author | Rio, Daniel E. Rawlings, Robert R. Woltz, Lawrence A. Gilman, Jodi Hommer, Daniel W. |
| Copyright Year | 2013 |
| Abstract | A linear time-invariant model based on statistical time series analysis in the Fourier domain for single subjects is further developed and applied to functional MRI (fMRI) blood-oxygen level-dependent (BOLD) multivariate data. This methodology was originally developed to analyze multiple stimulus input evoked response BOLD data. However, to analyze clinical data generated using a repeated measures experimental design, the model has been extended to handle multivariate time series data and demonstrated on control and alcoholic subjects taken from data previously analyzed in the temporal domain. Analysis of BOLD data is typically carried out in the time domain where the data has a high temporal correlation. These analyses generally employ parametric models of the hemodynamic response function (HRF) where prewhitening of the data is attempted using autoregressive (AR) models for the noise. However, this data can be analyzed in the Fourier domain. Here, assumptions made on the noise structure are less restrictive, and hypothesis tests can be constructed based on voxel-specific nonparametric estimates of the hemodynamic transfer function (HRF in the Fourier domain). This is especially important for experimental designs involving multiple states (either stimulus or drug induced) that may alter the form of the response function. |
| Related Links | http://dx.doi.org/10.1155/2013/645043 |
| Starting Page | 645043 |
| File Format | |
| ISSN | 1748670X |
| e-ISSN | 17486718 |
| Journal | Computational and Mathematical Methods in Medicine |
| Volume Number | 2013 |
| Language | English |
| Publisher | Hindawi Publishing Corporation |
| Publisher Date | 2013-01-01 |
| Access Restriction | Open |
| Rights Holder | Hindawi Publishing Corporation |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Modelling and Simulation Immunology and Microbiology(all) Applied Mathematics Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology Modeling and Simulation |
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