The Renyi entropy in data-driven analysis for pharmacological MRI

McGonigle John; Majid Mirmehdi; Holmes Robin; Malizia Andrea L.

The Renyi entropy in data-driven analysis for pharmacological MRI

McGonigle John, Majid Mirmehdi, Holmes Robin, Malizia Andrea L.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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

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http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2001203

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title = "The Renyi entropy in data-driven analysis for pharmacological MRI",

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. ",

author = "McGonigle John and Majid Mirmehdi and Holmes Robin and {Andrea L.}, Malizia",

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AU - Mirmehdi, Majid

AU - Robin, Holmes

AU - Andrea L., Malizia

N1 - Other page information: - Conference Proceedings/Title of Journal: Joint Annual Meeting ISMRM-ESMRMB Other identifier: 2001203

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

M3 - Conference Contribution (Conference Proceeding)

BT - Joint Annual Meeting ISMRM-ESMRMB

ER -