> Require an estimation of the noise at the level of the sensors (noise covariance matrix). * '''Dipole modeling''': ? * '''LCMV beamformer''': ?<
>Require both a noise covariance matrix and a data covariance matrix (representation of the effect we are trying to localize in the brain, covariance of the latencies of interest). * '''Minimum norm vs beamformer''': Provided that we know at what latencies to look at, we can compute a correct data covariance matrix and may obtain a better spatial accuracy with a beamformer. However, in many cases we don't exactly know what we are looking at, the risks of misinterpretation of badly designed beamforming results are high. Brainstorm tends to favor minimum norm solutions, which have the advantage of needing less manual tuning for getting acceptable results. === Measure === * '''wMNE''': Whitened and depth-weigthed linear L2-minimum norm estimates algorithm inspired from Matti Hamalainen's MNE software. For a full description of this method, please refer to the MNE manual, section 6, "The current estimates". [[http://www.nmr.mgh.harvard.edu/meg/manuals/MNE-manual-2.7.pdf|Download MNE manual here]]. * '''dSPM''': Noise-normalized estimate (dynamical Statistical Parametric Mapping [Dale, 2000]). Its computation is based on the wMNE solution. <
>Basically, the dSPM value at each location is equal to the wMNE value divided by the projection of the estimated noise covariance matrix onto each source point. After whitening, the operational noise covariance matrix is by definition the identity matrix, and hence the projection of the noise is equal to the L2 norm of the row vector of the wMNE inverse operator (in the case of fixed dipole orientations). So, dSPM is what you get when the rows of the wMNE inverse operator all have unit norm (i.e., they all point in different directions but lie in a unit hyper-sphere). * '''sLORETA''': Noise-normalized estimate using the sLORETA approach (standardized LOw Resolution brain Electromagnetic TomogrAphy [Pasqual-Marqui, 2002]). sLORETA solutions have in general a smaller location bias than either the expected current (wMNE) or the dSPM. * '''MNp''': === Source orientation === * '''Constrained''': We consider that at each vertex of the cortex surface, there is only one dipole, and that its orientation is the normal to the cortex surface at this point. * The size of the inverse operator is [nVertices x nChannel]. * This is based on the anatomical observation that in the cortex, the neurons are mainly organized in macro-columns that are perpendicular to the cortex surface. But it's hard to know if we can really rely on it at this level, for this algorithm: Is it the case everywhere in the cortex? Are we supposed to use the inner (grey matter/white matter) or the outer (grey/CSF) surface of the cortex? Can we really be that precise in terms of MRI/MEG registration? These are questions that do not have final answers yet. * A technical advantage of this method, it produces one value per vertex instead of three. As a consequence 1) the output size is smaller, 2) it's faster to compute and display, and 3) the results are much more intuitive to display because we don't have to think about how to combine three values in one on a cortical map in a 3D figure * '''Unconstrained''': At each vertex of the cortex surface, we define a base of three dipoles with orthogonal directions, and then we estimate the sources for the three orientations independently.The size of the inverse operator is [3*nVertices x nChannel]. * '''Loose''': A version of the "unconstrained" method that integrates a weak orientation constraint. It generates an inverse operator that is the same size as the unconstrained one, but that emphasizes the importance of the sources that have an orientation that is close to the normal to the cortex. The value associated with this option set how "loose" should be the orientation constrain (recommended values in MNE are between 0.1 and 0.6, --loose option). This is the default in MNE software. * '''Default''': At the present time, the default in Brainstorm is the "constrained" option, but we will probably switch to the unconstrained model soon. Because 1) we are not exactly sure that the orientation constraint is 100% correct, 2) the unconstrained sources look smoother and nicer, 3) the computation and storage capacities of the average computer increased a lot since the 1990s, so we can now afford to multiply by three the size of all the data. But right now, there are still some issues to fix in the processing pipeline of the unconstrained sources. == Computing sources for a single data file == '''CONSTRAINED MN (if it looks good): DEVIANT AVERAGE''' 1. Right-click on ''Subject01 / Right / ERF'' > ''Compute sources''.<
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> {{attachment:popupComputeSources.gif}} --- {{attachment:panelComputeSources.gif}} 1. With this window you can select the method you want to use to estimate the cortical currents, and the sensors you are going to use for this estimation. The default "Normal mode" only let you edit the following options:<
> * '''Comment''': This field contains what is going to be displayed in the database explorer. * '''Method''': Please select wMNE. The other methods dSPM and sLORETA are also based on wMNE. They may give better and/or smoother results depending on the cases. * '''Sensors type''': Modalities that are used for the reconstruction. Here we only have one type of MEG sensors (axial gradiometers), so nothing to change. * '''Expert mode''': Show other options we do not care about right now. * Click on Run. 1. A new file is available in the database explorer.<
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> {{attachment:treeMinNorm.gif}} * It is displayed'' inside ''the recordings file ERF, because it is related to this file only. * Meaning of that weird filename: "MN" stands for "Minimum Norm", and "Constr" stands for "Constrained orientation" of the dipoles (the estimated dipoles orientations are constrained to be normal to the cortex). * You can have a look to the corresponding matrix file (right-click > File > View file contents). You would find all the options of forward and inverse modeling, and only one interesting field : '''ImagingKernel''', which contains the inversion kernel. It is a [nVertices x nChannels] matrix that has to be multiplied with the recordings matrix in order to get the activity for each source at all the time samples. * The minimum norm solution being a linear operation (the time series for each source is a linear combination of all the time series recorded by the sensors), we make this economy of saving only this linear operator instead of the full source matrix (nVertices x nTime)<
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> 1. Do the same for the ''Left / ERF'' file == Display: Cortex surface == 1. Double-click on recordings ''Right / ERF'', to display the time series (always nice to have a time reference). 1. Double-click on sources ''Right / ERF / MN: MEG''. <
>Equivalent to right-click > Cortical activations > Display on cortex. 1. Go to the main peak around 46ms (by clicking on the times series figure)<
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> {{attachment:sources1.gif}} 1. Then you can manipulate the sources display exactly the same way as the surfaces and the 2D/3D recordings figures: rotation, zoom, ''Surface ''tab(smoothing, sulci, resection...), colormap, sensors, predefined orientations (keys from 0 to 7)... 1. Three new controls are available in the ''Surfaces ''tab, in panel ''Data options'': * '''Amplitude''': Only the sources that have a value superior than a given percentage of the colorbar maximum are displayed. * '''Min. size''': Hide all the small activated regions, ie. the connected color patches that contain a number of vertices smaller than this "min.size" value. * '''Transparency''': Change the transparency of the sources on the cortex. 1. Take a few minutes to understand what this threshold value represents.<
> * The colorbar maximum depends on the way you configured your ''Sources ''colormap. In case the colormap is NOT normalized to current time frame, and the maximum is NOT set to a specific value, the colorbar maximum should be around 68 pA.m. * On the screenshot above, the threshold value was set to 35%. It means that only the sources that had a value over 0.35*68 = 23.8 pA.m were visible. * If you set the threshold to 0%, you display all the sources values on the cortex surface; and as most of the sources have values close to 0, the brain is mainly blue. * Move the slider and look for a threshold value that would give you a really focal source.The following figures represent the sources activations at t=46ms respectively with threshold at 0% and 90%.<
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> {{attachment:threshold0.gif}} {{attachment:threshold90.gif}} * The figure on the right shows the most active area of the cortex 46ms after an electric stimulation of the right thumb. As expected, it is localized in the left hemisphere, in the middle of post central gyrus (projection of the right hand in the primary somatosensory cortex). == Display: MRI 3D == 1. Close all the figures (''Close all'' button). Open the time series view for Right / ERF. 1. Right-click on Right / ERF / MN: MEG > Cortical activations > Display on MRI (3D). 1. This view was also introduced in the tutorial about MRI and surface visualization. Try to rotate, zoom, move the slices, move in time, change the threshold.<
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> {{attachment:sources3D.gif}} {{attachment:popupFigMri.gif}} 1. A new menu is available in the popup menu of this figure: MRI Display * '''MIP Anatomy''': for each slice, display the maximum value over all the slices instead of the original value in the structural MRI (fig 1) * '''MIP Functional''': same thing but with the layer of functional values (fig 2) * '''Smooth level''': The sources values are smoothed after being re-interpolated in the volume. These menus define the size of the smoothing kernel (fig2: smooth=2; fig3: smooth=5).<
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> {{attachment:mriMipAnat.gif||height="143px",width="176px"}} {{attachment:mriMipFunc.gif||height="142px",width="175px"}} {{attachment:mriSmooth.gif||height="141px",width="173px"}} == Display: MRI Viewer == 1. Right-click on Right / ERF / MN: MEG > Cortical activations > Display on MRI (MRI Viewer). 1. This view was also introduced in the tutorial about MRI and surface visualization. Try to move the slices (sliders, mouse wheel, click on the views), move in time, change the threshold.<
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> {{attachment:sourcesMriViwer.gif||height="331px",width="359px"}} == Display: Contact sheets and movies == * '''Standard:''' (Right-click on the 3D figures > Snapshot > Time contact sheet) . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_standard_left.gif|average_source_standard_left.gif|height="263",width="486",class="attachment"}} * '''Deviant:''' . {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_deviant_left.gif|average_source_deviant_left.gif|height="263",width="486",class="attachment"}} * Contact sheets: in time or in space, for each orientation. You can try all the menus. Example: Right-click on the figure > Snapshot > Volume contact sheet: axial: <
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> {{attachment:popupSnapshot.gif}} {{attachment:contactAxial.gif||height="288px",width="322px"}} * '''Movies''': Right-click on any figure > Snapshot > '''Movie (time): All figures''' (click to download video) [[http://neuroimage.usc.edu/wikidocs/average_sources.avi|{{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=average_source_video.gif|http://neuroimage.usc.edu/brainstorm/wikidocs/average_sources.avi|height="258",width="484",class="attachment"}}]] == Minimum norm values are not only positive == You should pay attention to a property of the current amplitudes that are given by the wMNE method: they can be positive of negative, and they oscillate around zero. It's not easy to figure out what is the exact meaning of a negative value respect with a positive value, and most of the time we are only interested in knowing what is activated at what time, and therefore we look only at the absolute values of the sources. In some other cases, mainly when doing frequency analysis, we need to pay attention to the sign of these values. Because we cannot do a frequency decomposition of the absolute values of the sources, we need to keep the sign all along our processes. Display again the sources for Right / ERF on the cortex surface (double-click on the source file), and uncheck the Absolute option for the colormap "Sources" (right-click on the figure > Colormap Sources > Absolute values). Decrease the threshold to observe the pattern of alternance between positive and negative values on the surface. Then double click on the colorbar to reset it to its default. {{attachment:relValues.gif}} == Source map normalization == * MNp, dSPM, sLORETA * Z-score : baseline correction == Computing sources for multiple data files == '''UNCONSTRAINED MN''' The sources file we are observing was computed as an ''inversion kernel''. It means that we can apply it to any similar recordings file (same subject, same run, same positions of sensors). But in our TutorialCTF database, the ''MN: MEG'' node only appears in the the ''ERF ''file, not in the ''Std ''one. What is it necessary to share an inversion kernel between different recordings ? 1. Compute another source estimation: but instead of clicking on the ''Compute sources'' from the ''ERF ''recordings popup menu (which would mean that you only want sources for this particular recordings file), get this menu from the ''Right''''' condition'''. This means that you want the inversion model to be applied to all the data in the condition. 1. Select "Minimum Norm Imaging", click on Run. 1. Three new nodes are available in the tree:<
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