fMRI

Neuroimaging methods clinic

Tips, trick and support

Software and Toolboxes:

fMRI analysis packages:

The two most popular analysis packages for fMRI that provide a wide range of analyses and extensive support are FSL and SPM.

SPM
Requires Matlab but runs on all platforms. Check out the video course if you want to get started.
www.fil.ion.ucl.ac.uk/spm/
FSL
Runs on Apple OS or Linux PC. Can be run on a Windows platform inside a virtual machine. Check out the course if you want to get started.
fsl.fmrib.ox.ac.uk/fsl/fslwiki/

Resting State fMRI Toolboxes

REST for rsfMRI preprocesing. restfmri.net/forum/REST
GIFT for identification of resting state networkmialab.mrn.org/software/gift/

Connectivity Toolboxes

CONN toolbox www.nitrc.org/projects/conn
Mute toolbox (by A. Moltanto) Made @ UGent!
mutetoolbox.guru/

Power calculation Toolboxes

Neuropower (by J. Durnez): Web-based application. Made @ UGent!
https://neuropower.shinyapps.io/neuropower
fMRIpower (by J. Mumford)
fmripower.org/

Pattern Recognition Toolboxes

PRONTO toolbox: A nice toolbox if you have no previous experience. Requires Matlab and SPM. The website provides extensive support (manual, reference paper, course information, ...).
www.mlnl.cs.ucl.ac.uk/pronto/
PyMVPA toolbox: Command-line python package intended to ease statistical learning analyses of large datasets. Requires only free software to run.
http://www.pymvpa.org

Suggested courses:

fMRI course on coursera by Martin Lindquist & Tor Wager

If you haven't checked out coursera yet, you should! It provides free online courses from universities all over the world.
This course covers the design, acquisition, and analysis of fMRI data.

https://www.coursera.org/course/fmri1

https://www.coursera.org/course/fmri2

Jeanette Mumford brainstats

Check out Jeanette Mumford's facebook group (Mumfordbrainstats)!

Jeanette is a great teacher and provides short videos covering a wide range of topics in statistical modelling in fMRI.

The videos are also available in an archive: mumfordbrainstats.tumblr.com/archive

Relevant Papers:

Introductory overview of the statistical analysis of fMRI data.

Lindquist, M.A. (2008). The Statistical Analysis of fMRI Data, Statistical Science, 23(4), 439-464.
projecteuclid.org/euclid.ss/1242049389

Guidelines for reporting an fMRI study

Poldrack, R.A., Fletcher, P.C., Henson, R.N., Worsley, K.J., Brett, M., Nichols, T.E. (2008). Guidelines for reporting an fMRI study, Neuroimage, 40, pp. 409–414 www.ncbi.nlm.nih.gov/pubmed/18191585
On the way: Committee on Best Practices in Data Analysis and Sharing (COBIDAS) to identify best practices of data analysis and data sharing in the brain mapping community. You can still add suggestions and join the discussion!

To be significant or not to be significant…

Why do we need multiple testing corrections in fMRI? What procedures are available and how should we interpret our results?

Lindquist, M.A., Mejia, A. (2015). Zen and the Art of Multiple Comparisons, Psychosomatic Medicine, 77, 114-125. www.ncbi.nlm.nih.gov/pubmed/25647751

On orthogonality and parametric modulations and how to do it in FSL and SPM
How to optimally model complex designs:

Mumford, J.A., Poline, J.B., Poldrack, R.A. (2015). Orthogonalization of Regressors in fMRI Models, PLOS ONE, 10, e0126255. journals.plos.org/plosone/article?id=10.1371/journal.pone.0126255

Conjunctions
In SPM conjunctions come in two flavours. What is the difference? Which one do I need and how do I interpret my results?

Nichols, T.E., Brett, M., Andersson, J., Wager, T., Poline, J.B. (2005). Valid Conjunction Inference with the Minimum Statistic, NeuroImage, 25, 653-660. www.ncbi.nlm.nih.gov/pubmed/15808966

Pattern Recognition

Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. NeuroImage, 45, 199–209. www.ncbi.nlm.nih.gov/pubmed/19070668

Resting state fMRI

Biswal, B. B. (2012). Resting state fMRI: A personal history. [Review]. Neuroimage, 62(2), p. 938-944.
Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, 50(1), 81-98.

Resting state networks

Templates: http://www.brainnexus.com/resources/resting-state-fmri-templates
J. S. Damoiseaux, S. A. R. B. Rombouts, F. Barkhof, P. Scheltens, C. J. Stam, S. M. Smith, and C. F. Beckmann. Consistent resting-state networksacross healthy subjects. PNAS, 2006 (vol. 103/3).
Diez I, Erramuzpe A, Escudero I, Mateos B, Cabrera A, Marinazzo D, Sanz-Arigita EJ, Stramaglia S, Cortes Diaz JM. Information Flow Between Resting-State Networks. Brain Connect. 2015.

Connectivity measures

Wu GR, Stramaglia S, Chen H, Liao W, Marinazzo D. Mapping the voxel-wise effective connectome in resting state FMRI.

PLoS One. 2013
Wu GR, Liao W, Stramaglia S, Ding JR, Chen H, Marinazzo D. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med Image Anal. 2013
Liao W, Wu GR, Xu Q, Ji GJ, Zhang Z, Zang YF, Lu G. DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connect. 2014
Montalto A, Faes L, Marinazzo D.MuTE: a MATLAB toolbox to compare established and novel estimators of the multivariate transfer entropy. PLoS One. 2014