Functional Localizer
This is a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level. Individual functional maps are reliable and quite precise. In Pinel et al. 2007 we showed that 80% of main maxima (isolated with a 30-minute long acquisition) were detected in this 5-minute design, with an average spatial precision of 6mm. This script is written using the E-Prime1 software (Psychology Software Tool, Inc.). However, it is automatically converted for E-Prime2 when opened in this E-Prime version.
Download the Functional Localizer
Here you can download the Functional Localizer in various languages:
in French
in English
in Portuguese
in Brazilian
in Italian
All of them require a video resolution of about 640 x 480 pixels
An example of Functional Localizer for an higher resolution (as 1024 x 768 used in Neurospin) is given here in French (See below to adapt the Localizer to other resolution).
We also propose the Localizer in a slower version in French (for patients, for instance), still not used yet in a published work. Stimuli are the same as in the short version, but we added 1800 msec in the inter-task interval (and onsets have then to be adapted. See below to download onset and conditions files).
Using the Functional Localizer.
When using this protocol for a paper please reference the following article :
Pinel P, Thirion B, Meriaux S, Jobert A, Serres J, Le Bihan D, Poline JB and Dehaene S. (2007). Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neuroscience, 8, 91.
Please, send us an email to keep in touch with us if you plan to include this Functional Localizer to your protocol, and write to philippe.pinel@cea.fr for any question about it. You are also welcome to add to our site your own translation and recording of stimuli in your own language or accent (Spanish, Danish, Japanese or other…).
Acquisition parameters and devices
This functional protocol requires:
▪ A video screen and projector for visual stimuli
▪ Earphones for auditory stimuli
▪ Two response buttons (usually, subjects’ response are not analyzed. You can eventually just simulate button response) for the left and right thumbs.
To acquire the localizer we use a TR of 2.4 sec, and try to cover all the brain (except eventually a part of the cerebellum). As the localizer is about 5-mn, we acquire 128 images (resolution is 3x3x3mm. Initial image size = 64x64x40 voxels, meaning 40 axial slices of 3mm).
How to run the Functional Localizer
When you run the script, different options are proposed:
Press (c) using this start a short sequence during which you can test the position of the center of the stimuli in the subject’s vision field, the level of audio stimuli and the motor responses. You should do that when the subject just get inside the scanner, just before the beginning of acquisition. Usually, you can force the script to go to the next screen by using the space bar (especially to replace the subject’s button response).
Press (i) using this start a 1.5-minute sequence of video instruction.You can do that
when the subject is installed in the scanner, during anatomy acquisition for instance
Press (e) start the experiment. You then have four different possible sessions. In French, this 4 sessions are used to eventually repeat the Localizer (same onset and structure) but with different stimuli. In other language, only the first session (f) is available, but all translated stimuli are listed in All-Stimuli-4session-english.xls for English version. You must use (k) after the experiment to correctly record trials onsets and subject’s response I the E-Data format. If the subject wants to see again the instruction, you can go to (i).
Press (s) to exit the script.
Useful parameters to be adapted
After you start the experiment, the script waits for the scanner TTL. In the TTL box, the parallel port signal is recognized as “6”. In procedure ‘BoutonAudio’ and in the procedure ‘BoutonVideo’ (in the box ‘mot4clic’), motor response are coded as “4” (right button) and “5” (left button)
Adapt your video stimuli to your screen resolution.
The standard version of the Functional Localizer uses a video resolution of 640 x 480 pixels (except for the Localizer_Francais_HighVideoResoultion). If you want to use a higher resolution, you have to multiply the size of text font (within the script) and the size of the video stimuli (checkerboard) by the ratio between your own video resolution and this initial resolution. For instance:
Video resolution: 640 x 480 pixels
c.SetAttrib "FontInstruction", 11
c.SetAttrib "FontVideoStimuli", 15
horizontal checkerboard dimensions 368x276
Video Resolution: 1024 x 768 pixels
c.SetAttrib "FontInstruction", 17
c.SetAttrib "FontVideoStimuli", 24
horizontal checkerboard dimensions 588 x 441
Because resolution was increased with a 1.6 ratio, all dimensions where multiplied by 1.6. Of course, you can reversely use the stimuli adapted to1024 x 768 to decrease the size of stimuli to your resolution.
Analysis of this protocol: onsets, conditions & model
Because the onsets (msec) of this protocol and condition order are the same for all sessions, you can directly use the timing from this file, without extracting data from the E-data file generated at the end of the experiment. Conditions are ranged from 1 to 10, 11 meaning blank, or rest, trials and then removed from the model). The first onset is 0 (msec), which is also the begining of the first scan. You can also used the 4 first blank trials of 3 sec (not used but present in our script), that correspond to 5 dumb scans of 2.4 TR (if your scanner does not do itself the signal stabilization procedure). You can download here the triasl conditions and onsets, or the Matlab script we use to specify the statistical model and the functional contrasts.
All articles that used and cited this protocol
Pinel P, Fauchereau F, Moreno A, Barbot A, Lathrop M, Zelenika D, Le Bihan D, Poline JB, Bourgeron T, Dehaene S. (2012). Genetic Variants of FOXP2 and KIAA0319/TTRAP/THEM2 Locus Are Associated with Altered Brain Activation in Distinct Language-Related Regions. Journal of Neuroscience 18, 817-825.
S. Dehaene, F. Pegado, L.W. Braga, P. Ventura, G. Nunes Filho, A. Jobert, G. Dehaene-Lambertz, R. Kolinsky, J. Morais, and L. Cohen. (2010). How Learning to Read Changes the Cortical Networks for Vision and Language. Science 330, 1359-1364.
G. Varoquaux, Sadaghiani S, Pinel P, Kleinschmidt A, Poline JB, Thirion B. (2010) A group model for stable multi-subject ICA on fMRI datasets. NeuroImage 51(1), 288-299.
Pinel P and Dehaene S. (2009). Beyond Hemispheric Dominance: Brain Regions Underlying the Joint Lateralization of Language and Arithmetic to the Left Hemisphere. Journal of Cognitive Neuroscience 22, 48-66.
Tucholka A, Thirion B, Perrot M, Pinel P, Mangin JF and Poline JB.(2008) Probabilistic anatomo-functional parcellation of the cortex: how many regions? Med Image Comput Assist, 11(Pt 2), 399-406.
Pinel P, Thirion B, Meriaux S, Jobert A, Serres J, Le Bihan D, Poline JB and Dehaene S. (2007). Fast reproducible identification and large-scale databasing of individual functional cognitive networks. BMC Neuroscience, 8, 91.
Piazza M., Pinel P., Le Bihan D., Dehaene S. (2007) A Magnitude code common to numerosities and number symbols in human intraparietal cortex. Neuron (53), 293-305.
Thirion B, Pinel P, Tucholka A, Roche A, Ciuciu P, Mangin JF, Poline JB. (2007). Structural analysis of fMRI data revisited: improving the sensitivity and reliability of fMRI group studies. IEEE Trans. Med. Imaging 26(9),1256-1269.
Thirion B., Pinel P., Meriaux S., Roche A., Dehaene S., Poline JB. (2007) Analysis of a large fMRI cohort : Statistical and methodological issues for group analyses. NeuroImages 35(1), 105-120.
Thirion B, Tucholka A, Keller M, Pinel P, Roche A, Mangin JF and Poline JB. (2007). High level group analysis of FMRI data based on Dirichlet process mixture models. Inf. Process. Med. Imaging 20, 482-494.
Thirion B., Flandin G., Pinel P., Roche A., Ciuciu P., and Poline J.-B. (2005) Finding landmarks in the functional brain : detection and use for group characterization. Med Image Comput Assist, (8), 476-483.
Thirion B., Flandin G., Pinel P., Roche A., Ciuciu P., and Poline J.-B. (2005) Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Human Brain Mapping 27(8), 678-693.