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Proceedings Paper

A task-related and resting state realistic fMRI simulator for fMRI data validation
Author(s): Jason E. Hill; Xiangyu Liu; Brian Nutter; Sunanda Mitra
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Paper Abstract

After more than 25 years of published functional magnetic resonance imaging (fMRI) studies, careful scrutiny reveals that most of the reported results lack fully decisive validation. The complex nature of fMRI data generation and acquisition results in unavoidable uncertainties in the true estimation and interpretation of both task-related activation maps and resting state functional connectivity networks, despite the use of various statistical data analysis methodologies. The goal of developing the proposed STANCE (Spontaneous and Task-related Activation of Neuronally Correlated Events) simulator is to generate realistic task-related and/or resting-state 4D blood oxygenation level dependent (BOLD) signals, given the experimental paradigm and scan protocol, by using digital phantoms of twenty normal brains available from BrainWeb (http://brainweb.bic.mni.mcgill.ca/brainweb/). The proposed simulator will include estimated system and modelled physiological noise as well as motion to serve as a reference to measured brain activities. In its current form, STANCE is a MATLAB toolbox with command line functions serving as an open-source add-on to SPM8 (http://www.fil.ion.ucl.ac.uk/spm/software/spm8/). The STANCE simulator has been designed in a modular framework so that the hemodynamic response (HR) and various noise models can be iteratively improved to include evolving knowledge about such models.

Paper Details

Date Published: 24 February 2017
PDF: 12 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332N (24 February 2017); doi: 10.1117/12.2254777
Show Author Affiliations
Jason E. Hill, Texas Tech Univ. (United States)
Xiangyu Liu, Texas Tech Univ. (United States)
Brian Nutter, Texas Tech Univ (United States)
Sunanda Mitra, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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