Abstract
The analysis of pharmacological MRI (phMRI) traditionally depends upon the use of an appropriate input function, usually derived from blood plasma concentrations of the drug used in the experiment. There are a number of problems with this approach including the relationship between plasma and brain concentrations and the longer term effects of receptor activation. Because of this a number of data-driven approaches have been
used where no model of the neural response is known a priori such as independent component analysis and wavelet cluster analysis. Here we explore the use of a measure of signal complexity known as the Renyi entropy to discover voxels of interest in a data-driven manner using a dataset known to show reduced perfusion in the hippocampus.
| Translated title of the contribution | The Renyi entropy in data-driven analysis for pharmacological MRI |
|---|---|
| Original language | English |
| Title of host publication | Joint Annual Meeting ISMRM-ESMRMB |
| Publication status | Published - 2010 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Joint Annual Meeting ISMRM-ESMRMB
Other identifier: 2001203