Analytic Programming with FMRI Data: a Quick-start Guide for Statisticians Using R

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2014 Mar 4

PMID 24586801

Citations 4

Authors

Ani Eloyan

Shanshan Li

John Muschelli

Jim J Pekar

Stewart H Mostofsky

Brian S Caffo

Affiliations

Soon will be listed here.

Abstract

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

Citing Articles

Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data.

Zhang R, Zhang Y, Liu Y, Guo Y, Shen Y, Deng D Neural Comput Appl. 2022; 34(8):6377-6396.

PMID: 35936508 PMC: 9355340. DOI: 10.1007/s00521-021-06789-8.

Oculocardiac Reflex During Intravitreal Injection.

Paciuc-Beja M, Meizner-Grezemkovsky D, Paciuc M, Sanchez-Santos I, Ruiz-Roman A, Fack A Med Hypothesis Discov Innov Ophthalmol. 2020; 9(2):111-117.

PMID: 32490017 PMC: 7134247.

Bayesian modeling of dependence in brain connectivity data.

Chen S, Xing Y, Kang J, Kochunov P, Hong L Biostatistics. 2018; 21(2):269-286.

PMID: 30203093 PMC: 7868047. DOI: 10.1093/biostatistics/kxy046.

Thou Shalt Be Reproducible! A Technology Perspective.

Mair P Front Psychol. 2016; 7:1079.

PMID: 27471486 PMC: 4943952. DOI: 10.3389/fpsyg.2016.01079.

References

Wager T, Nichols T . Optimization of experimental design in fMRI: a general framework using a genetic algorithm. Neuroimage. 2003; 18(2):293-309. DOI: 10.1016/s1053-8119(02)00046-0. View

Turner R, Jezzard P, Wen H, Kwong K, Le Bihan D, Zeffiro T . Functional mapping of the human visual cortex at 4 and 1.5 tesla using deoxygenation contrast EPI. Magn Reson Med. 1993; 29(2):277-9. DOI: 10.1002/mrm.1910290221. View

Zhou J, Wilson D, Ulatowski J, Traystman R, van Zijl P . Two-compartment exchange model for perfusion quantification using arterial spin tagging. J Cereb Blood Flow Metab. 2001; 21(4):440-55. DOI: 10.1097/00004647-200104000-00013. View

Worsley K, Liao C, Aston J, Petre V, Duncan G, Morales F . A general statistical analysis for fMRI data. Neuroimage. 2002; 15(1):1-15. DOI: 10.1006/nimg.2001.0933. View

Tabelow K, Polzehl J, Voss H, Spokoiny V . Analyzing fMRI experiments with structural adaptive smoothing procedures. Neuroimage. 2006; 33(1):55-62. DOI: 10.1016/j.neuroimage.2006.06.029. View

Smith S . Overview of fMRI analysis. Br J Radiol. 2005; 77 Spec No 2:S167-75. DOI: 10.1259/bjr/33553595. View

Henson R, Shallice T, Gorno-Tempini M, Dolan R . Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cereb Cortex. 2001; 12(2):178-86. DOI: 10.1093/cercor/12.2.178. View

Miller M, Christensen G, Amit Y, Grenander U . Mathematical textbook of deformable neuroanatomies. Proc Natl Acad Sci U S A. 1993; 90(24):11944-8. PMC: 48101. DOI: 10.1073/pnas.90.24.11944. View

Caffo B, Crainiceanu C, Verduzco G, Joel S, Mostofsky S, Bassett S . Two-stage decompositions for the analysis of functional connectivity for fMRI with application to Alzheimer's disease risk. Neuroimage. 2010; 51(3):1140-9. PMC: 2862769. DOI: 10.1016/j.neuroimage.2010.02.081. View

10.

Friston K, Frith C, Turner R, Frackowiak R . Characterizing evoked hemodynamics with fMRI. Neuroimage. 1995; 2(2):157-65. DOI: 10.1006/nimg.1995.1018. View

11.

Beckmann C, DeLuca M, Devlin J, Smith S . Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005; 360(1457):1001-13. PMC: 1854918. DOI: 10.1098/rstb.2005.1634. View

12.

Ogawa S, Lee T, Nayak A, Glynn P . Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med. 1990; 14(1):68-78. DOI: 10.1002/mrm.1910140108. View

13.

Hayasaka S, Phan K, Liberzon I, Worsley K, Nichols T . Nonstationary cluster-size inference with random field and permutation methods. Neuroimage. 2004; 22(2):676-87. DOI: 10.1016/j.neuroimage.2004.01.041. View

14.

Turner R, Le Bihan D, Moonen C, Despres D, Frank J . Echo-planar time course MRI of cat brain oxygenation changes. Magn Reson Med. 1991; 22(1):159-66. DOI: 10.1002/mrm.1910220117. View

15.

Aguirre G, Zarahn E, DEsposito M . The variability of human, BOLD hemodynamic responses. Neuroimage. 1998; 8(4):360-9. DOI: 10.1006/nimg.1998.0369. View

16.

Tabelow K, Clayden J, Lafaye de Micheaux P, Polzehl J, Schmid V, Whitcher B . Image analysis and statistical inference in neuroimaging with R. Neuroimage. 2011; 55(4):1686-93. DOI: 10.1016/j.neuroimage.2011.01.013. View

17.

Ogawa S, Tank D, Menon R, Ellermann J, Kim S, Merkle H . Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A. 1992; 89(13):5951-5. PMC: 402116. DOI: 10.1073/pnas.89.13.5951. View

18.

Guo Y, Pagnoni G . A unified framework for group independent component analysis for multi-subject fMRI data. Neuroimage. 2008; 42(3):1078-93. PMC: 2853771. DOI: 10.1016/j.neuroimage.2008.05.008. View

19.

Worsley K, Taylor J . Detecting fMRI activation allowing for unknown latency of the hemodynamic response. Neuroimage. 2005; 29(2):649-54. DOI: 10.1016/j.neuroimage.2005.07.032. View

20.

Liu T, Frank L, Wong E, Buxton R . Detection power, estimation efficiency, and predictability in event-related fMRI. Neuroimage. 2001; 13(4):759-73. DOI: 10.1006/nimg.2000.0728. View