= Tutorial 22: Source estimation = '''[UNDER CONSTRUCTION]''' ''Authors: Francois Tadel, Elizabeth Bock, Rey R Ramirez, John C Mosher, Richard Leahy, Sylvain Baillet'' You have in your database a forward model matrix that explains how the cortical sources determine the values on the sensors. This is useful for simulations, but what we really need is to build the inverse information: how to estimate the sources when we have the recordings. This tutorials introduce the tools available in Brainstorm for solving this inverse problem. <> == Ill-posed problem == Our goal is now to estimate the activity of the 45,000 dipoles described by our forward model. However we only have a few tens or hundreds of variables to estimate this activity (the number of sensors). This inverse problem is ill-posed, there is an infinity of combinations of source activity that can generate exactly the same sensor topography. Inverting the forward problem directly is impossible, unless we add some strong priors in our model. Wikipedia says: "Inverse problems are some of the most important and well-studied mathematical problems in science and mathematics because they tell us about parameters that we cannot directly observe. They have wide application in optics, radar, acoustics, communication theory, signal processing, medical imaging, computer vision, geophysics, oceanography, astronomy, remote sensing, natural language processing, machine learning, nondestructive testing, and many other fields." Many solutions have been proposed in the literature, based on different assumptions on the way the brain works and depending on the amount of information we already have on the effects we are studying. Two classes of inverse models have been widely used some of them are implemented in Brainstorm, and only one is presented in this tutorial: the minimum-norm estimation. It is not really the most advanced solution, but it is one of the most robust. == Computing sources for a single data file == 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 == Sources visualization == There are two main ways to display the sources: on the cortex surface and on the MRI slices. === Sources on 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). === Sources on 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"}} 1. This view can be used to lots of different types of 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"}} === Sources on MRI (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"}} === 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}} === Minimum norm units === For information about the units used to represent the minimum norm source activation, pA.m (pico Ampere meter), please refer to the following forum post: [[http://neuroimage.usc.edu/forums/showthread.php?1246-Doubt-about-current-density-units-pA.m-or-pA-m2|http://neuroimage.usc.edu/...Doubt-about-current-density-units-pA.m-or-pA-m2]] == Computing sources for multiple data files == 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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