Literature DB >> 22178299

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability.

Erik B Erhardt1, Elena A Allen, Yonghua Wei, Tom Eichele, Vince D Calhoun.   

Abstract

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 22178299      PMCID: PMC3690331          DOI: 10.1016/j.neuroimage.2011.11.088

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  44 in total

1.  Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis.

Authors:  R Baumgartner; L Ryner; W Richter; R Summers; M Jarmasz; R Somorjai
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2.  An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

Authors:  Valeria Della-Maggiore; Wilkin Chau; Pedro R Peres-Neto; Anthony R McIntosh
Journal:  Neuroimage       Date:  2002-09       Impact factor: 6.556

3.  An evaluation of thresholding techniques in fMRI analysis.

Authors:  Brent R Logan; Daniel B Rowe
Journal:  Neuroimage       Date:  2004-05       Impact factor: 6.556

4.  Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains.

Authors:  E Bullmore; C Long; J Suckling; J Fadili; G Calvert; F Zelaya; T A Carpenter; M Brammer
Journal:  Hum Brain Mapp       Date:  2001-02       Impact factor: 5.038

5.  Estimating the number of independent components for functional magnetic resonance imaging data.

Authors:  Yi-Ou Li; Tülay Adali; Vince D Calhoun
Journal:  Hum Brain Mapp       Date:  2007-11       Impact factor: 5.038

Review 6.  A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation.

Authors:  R B Buxton; L R Frank
Journal:  J Cereb Blood Flow Metab       Date:  1997-01       Impact factor: 6.200

7.  The effect of model order selection in group PICA.

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Journal:  Hum Brain Mapp       Date:  2010-08       Impact factor: 5.038

8.  The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?

Authors:  Kevin Murphy; Rasmus M Birn; Daniel A Handwerker; Tyler B Jones; Peter A Bandettini
Journal:  Neuroimage       Date:  2008-10-11       Impact factor: 6.556

9.  Comparison of multi-subject ICA methods for analysis of fMRI data.

Authors:  Erik Barry Erhardt; Srinivas Rachakonda; Edward J Bedrick; Elena A Allen; Tülay Adali; Vince D Calhoun
Journal:  Hum Brain Mapp       Date:  2010-12-15       Impact factor: 5.038

10.  Development of a functional magnetic resonance imaging simulator for modeling realistic rigid-body motion artifacts.

Authors:  Ivana Drobnjak; David Gavaghan; Endre Süli; Joe Pitt-Francis; Mark Jenkinson
Journal:  Magn Reson Med       Date:  2006-08       Impact factor: 4.668

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  63 in total

1.  Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks.

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Journal:  Artif Intell Med       Date:  2020-05-12       Impact factor: 5.326

2.  Longitudinally consistent estimates of intrinsic functional networks.

Authors:  Qingyu Zhao; Dongjin Kwon; Eva M Müller-Oehring; Anne-Pascale Le Berre; Adolf Pfefferbaum; Edith V Sullivan; Kilian M Pohl
Journal:  Hum Brain Mapp       Date:  2019-02-25       Impact factor: 5.038

3.  Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity.

Authors:  Fatemeh Mokhtari; Paul J Laurienti; W Jack Rejeski; Grey Ballard
Journal:  Brain Connect       Date:  2018-12-26

4.  Extracting intrinsic functional networks with feature-based group independent component analysis.

Authors:  Vince D Calhoun; Elena Allen
Journal:  Psychometrika       Date:  2012-10-02       Impact factor: 2.500

5.  Two-step paretial least square regression classifiers in brain-state decoding using functional magnetic resonance imaging.

Authors:  Zhiying Long; Yubao Wang; Xuanping Liu; Li Yao
Journal:  PLoS One       Date:  2019-04-10       Impact factor: 3.240

6.  Investigating the impact of autocorrelation on time-varying connectivity.

Authors:  Hamed Honari; Ann S Choe; James J Pekar; Martin A Lindquist
Journal:  Neuroimage       Date:  2019-04-22       Impact factor: 6.556

7.  A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.

Authors:  Ryan Warnick; Michele Guindani; Erik Erhardt; Elena Allen; Vince Calhoun; Marina Vannucci
Journal:  J Am Stat Assoc       Date:  2018-05-16       Impact factor: 5.033

8.  Large-scale sparse functional networks from resting state fMRI.

Authors:  Hongming Li; Theodore D Satterthwaite; Yong Fan
Journal:  Neuroimage       Date:  2017-05-05       Impact factor: 6.556

9.  Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

Authors:  Heather Shappell; Brian S Caffo; James J Pekar; Martin A Lindquist
Journal:  Neuroimage       Date:  2019-02-10       Impact factor: 6.556

10.  Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data.

Authors:  Jinglei Lv; Xi Jiang; Xiang Li; Dajiang Zhu; Shijie Zhao; Tuo Zhang; Xintao Hu; Junwei Han; Lei Guo; Zhihao Li; Claire Coles; Xiaoping Hu; Tianming Liu
Journal:  Psychiatry Res       Date:  2015-07-09       Impact factor: 3.222

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