Effect Size Comparison for Gaussian and Rician Modelling within fMRI Data

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

It has been argued that due to the bias at low SNR,
the Gaussian approach is unsuitable for modelling Rician fMRI
data. As a result several estimators incorporating the Rician
nature of the data have been proposed to measure the signal
as accurately as possible.
However, within fMRI the main objective is not to measure
the signal, but rather to measure changes within the signal. As
an increasing function of the signal, the mean can be used for
this purpose as well. In this paper it is argued that, due to its
lower variance, the sample average is a more suitable tool to
detect changes in the amplitude than several conventional Rician
parameter estimators at those SNR values common within fMRI
measurements.
While the interpretation is slightly different, this Rician
mean-based approach is essentially equivalent to the Gaussian
approach. Despite its bias, the Gaussian approach is therefore
preferable within fMRI analysis.
Original languageEnglish
Title of host publication2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
Number of pages5
DOIs
Publication statusPublished - 16 Aug 2018
EventMEMEA 2018 - IEEE International Symposium on Medical Measurements & Applications - LA SAPIENZA University of Rome, Rome, Italy
Duration: 11 Jun 201813 Jun 2018
http://memea2018.ieee-ims.org/pages/home

Conference

ConferenceMEMEA 2018 - IEEE International Symposium on Medical Measurements & Applications
Abbreviated titleMEMEA
CountryItaly
CityRome
Period11/06/1813/06/18
Internet address

Keywords

  • Rice distribution
  • fMRI
  • Effect size
  • Rician distribution

Fingerprint

Dive into the research topics of 'Effect Size Comparison for Gaussian and Rician Modelling within fMRI Data'. Together they form a unique fingerprint.

Cite this