Les magnétomètres, les gradiomètres ? quel moyennage utiliser
Download pdf file: :gradiomagnetofren_v2.pdf|
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As seen by planar gradiometers, the cartography of the magnetic field gradients take the form of monopolar patterns with maxima just over the sources.
In the Elekta system the two planar gradiometers at a given measurement point are arranged orthogonally. Combining the two measures from the two gradiometers gives a vector from which one can derive two values:
A scalar value that is the norm of the vector; this value is always positive. You can perform usual measures of latency, amplitude, and associated statistics on this value but no computation involving frequency decomposition or source localization. The computation of the norm is not linear with respect to the magnetic field value (and thus with respect to the neuronal activity). The main advantage of the gradiometer norm is the location of the maximum just above the corresponding source.
A direction that is orthogonal to the direction of the source in the plan tangential to the scalp (thus the direction should be parallel to the sulcus wall). You can potentially study the stability of this direction at one given time trial by trial as it is done with phase synchrony computation.
Visualization of Elekta data
Muse can display cartography from the magnetometers data and gradiometers data (norm and direction).
{{ :gradiomagnetofren.jpg?direct&600 |}}
Handling gradiometer data for evoked potential computation
As stated above, the computation of the norm is not linear. Thus, computing the norm of the average and computing the average of the norm are not equivalent.
Within-subject averaging of evoked responses
Computing the norm of the gradiometer then averaging it is equivalent to average the intensity of the sources without taking into account their orientation. On a raw signal where both random sources and sources linked to the task are activated, it leads to an increase of the background noise (activities not linked to the task), because the repeatability of the orientation of the sources related to the task across trials contributes to the evoked response. In a given subject, with a stable head position in the helmet, the sources linked to the task should have the same orientation at one given time trial after trial. Thus computing first the average and then the norm should be preferred. You will obtain the intensity of the sources that are the most stable in direction.
Within subject difference
It is possible to compute difference between conditions A and B. Results will show positive (or negative values if activities was higher during A (respectively lower during A). Computing first the difference and then the norm will results in an always positive results which may prove difficult to interpret.
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dataHandler -r -planar AVG_COND_A.fif -new PLANAR_ |
Between-subject averaging (grand mean):
To compute a grand average between subjects, we will follow an opposite way. Indeed, on each subject the above described averaging gives stable sources linked to the stimulation and task but the directions of these sources is likely to be variable from one subject to another due both to the variability in anatomy and to the variability in subject’s position in the MEG helmet at the time of recording. For this reason, it is advised to compute first the norm of evoked responses and then their grand average across subjects. This allows increasing the signal to noise ratio of the grand average, whereas taking into account the direction would decrease it (if the direction are completely random you may end up with a quasi null value).
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As a result one of the gradiometers will be set to zero while the other will contain the averaged norm data.
Between subject difference (grand average of differences)
It is recommended to first get the averaged data in each subject (in cond A and cond B). Then compute the scalar value of this (-planar option; needs to be done in a second step); Then compute the difference of norms for condA minus condB, in each subjects, then grand average without the –planar option this time because otherwise we set all values to positive values.
Time-frequency computation
For gradiometers, at this point, we propose to compute time-frequency transform on each gradiometer, at every measurement point. The frequency spectrum power obtained on each gradiometer can then be averaged for each trial and across trials to obtained induced oscillatory activity. The order of the averaging does matter here, all power values are positive and thus no cancellation can occur.
Comment visualiser magnétomètres et gradiomètres ?
- CARTOFIFF necessite qu il y ait dans le fichier tous les magnetometres et tous les gradiometres.
Il ne faut surtout pas changer la selection de capteurs par defaut ( = tous les magnetometres )
La carto aura alors 3 layers: un pour les magnetometres, un pour la norme des gradiometres et un pour les vecteurs des gradiometres
Elle correspond aux fichiers classiques .fif, c'est a dire ceux qui stockent chacune des composantes des gradiometres separement (la norme n'est pas stockee dans le fichier, elle est calculee au vol au moment de la visu) - L "autre" carto est plus "tolerante" : elle autorise de regler la selection de capteurs, a condition que la selection contienne une seule categorie de capteurs (soit des magnetometres, soit des gradiometres, soit des EEG, ... et pas forcement tous, par exemple uniquement les gradiometres dont le nom se termine par 2). Par contre, elle ne prend absolument pas en compte l'information "vectorielle". Elle ignore qu un gradiometre 2 est associe a un gradiometre 3.
Pour des fichiers .fif classiques, elle permet par exemple de regarder uniquement les magnetometres ( <=> layer magnetometres de la CartoFIFF ), ou bien uniquement les gradiometres qui finissent par 2, ou bien uniquement les gradiometres qui finissent par 3 ou n importe quelle combinaison de gradiometres (ceux qui finissent par 2 et par 3 appartiennent a la meme categorie "gradiometre"). Dans ce cas, regarder uniquement les gradiometres 2 ou uniquement les gradiometres 3 a pas forcement beaucoup de sens (on regarde une seule composante d'un vecteur) et regarder tous les gradiometres simultanement non plus car on calcule une interpolation avec 2 valeurs differentes (celle du 2 et celle du 3) au meme point.
Par contre, si un fichier (calcule avec -planar) contient une norme dans les gradiometres 2 et une autre information dans les gradiometres 3, alors en selectionnant uniquement les gradiometres 2, on obtiendra une carto des normes (l information des gradiometres 3 sera ignoree).
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