Literature DB >> 23165015

BOLD Noise Assumptions in fMRI.

Alle Meije Wink1, Jos B T M Roerdink.   

Abstract

This paper discusses the assumption of Gaussian noise in the blood-oxygenation-dependent (BOLD) contrast for functional MRI (fMRI). In principle, magnitudes in MRI images follow a Rice distribution. We start by reviewing differences between Rician and Gaussian noise. An analytic expression is derived for the null (resting-state) distribution of the difference between two Rician distributed images. This distribution is shown to be symmetric, and an exact expression for its standard deviation is derived. This distribution can be well approximated by a Gaussian, with very high precision for high SNR, and high precision for lower SNR. Tests on simulated and real MR images show that subtracting the time-series mean in fMRI yields asymmetrically distributed temporal noise. Subtracting a resting-state time series from the first results in symmetric and nearly Gaussian noise. This has important consequences for fMRI analyses using standard statistical tests.

Entities:  

Year:  2006        PMID: 23165015      PMCID: PMC2324015          DOI: 10.1155/IJBI/2006/12014

Source DB:  PubMed          Journal:  Int J Biomed Imaging        ISSN: 1687-4188


  14 in total

1.  Frequency domain connectivity identification: an application of partial directed coherence in fMRI.

Authors:  João R Sato; Daniel Y Takahashi; Silvia M Arcuri; Koichi Sameshima; Pedro A Morettin; Luiz A Baccalá
Journal:  Hum Brain Mapp       Date:  2009-02       Impact factor: 5.038

2.  Activated region fitting: a robust high-power method for fMRI analysis using parameterized regions of activation.

Authors:  Wouter D Weeda; Lourens J Waldorp; Ingrid Christoffels; Hilde M Huizenga
Journal:  Hum Brain Mapp       Date:  2009-08       Impact factor: 5.038

3.  Evaluation of statistical inference on empirical resting state fMRI.

Authors:  Xue Yang; Hakmook Kang; Allen T Newton; Bennett A Landman
Journal:  IEEE Trans Biomed Eng       Date:  2014-04       Impact factor: 4.538

4.  Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

Authors:  Francesca Strappini; Elad Gilboa; Sabrina Pitzalis; Kendrick Kay; Mark McAvoy; Arye Nehorai; Abraham Z Snyder
Journal:  Hum Brain Mapp       Date:  2016-12-10       Impact factor: 5.038

5.  Effects of Compounded Nonnormality of Residuals in Hierarchical Linear Modeling.

Authors:  Kaiwen Man; Randall Schumacker; Monica Morell; Yurou Wang
Journal:  Educ Psychol Meas       Date:  2021-05-10       Impact factor: 2.821

6.  Robust and unbiased variance of GLM coefficients for misspecified autocorrelation and hemodynamic response models in fMRI.

Authors:  Lourens Waldorp
Journal:  Int J Biomed Imaging       Date:  2009-09-06

7.  Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.

Authors:  Martin Havlicek; Jiri Jan; Milan Brazdil; Vince D Calhoun
Journal:  Neuroimage       Date:  2010-06-01       Impact factor: 6.556

8.  Fast random permutation tests enable objective evaluation of methods for single-subject FMRI analysis.

Authors:  Anders Eklund; Mats Andersson; Hans Knutsson
Journal:  Int J Biomed Imaging       Date:  2011-10-23

9.  Optimized design and analysis of sparse-sampling FMRI experiments.

Authors:  Tyler K Perrachione; Satrajit S Ghosh
Journal:  Front Neurosci       Date:  2013-04-18       Impact factor: 4.677

10.  Using fMRI non-local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography.

Authors:  Michaël Bernier; Maxime Chamberland; Jean-Christophe Houde; Maxime Descoteaux; Kevin Whittingstall
Journal:  Front Hum Neurosci       Date:  2014-09-11       Impact factor: 3.169

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