* We have to be careful with the visual comparisons of constrained and unconstrained source maps displayed on the cortex surface, because they are very different types of data. In unconstrained source maps, we have '''three dipoles with orthogonal orientations at each cortex location''', therefore we cannot represent all the information at once. To display them as an activity map, Brainstorm computes the '''norm of the vectorial sum of the three orientations at each vertex'''. <<BR>>S = sqrt(Sx<<HTML(&sigma;<SUP>2</SUP>)>> + Sy<<HTML(&sigma;<SUP>2</SUP>)>> + Sz<<HTML(&sigma;<SUP>2</SUP>)>>) <<BR>><<BR>> {{attachment:minnorm_unconstr_sketch.gif||height="158",width="476"}} * We have to be careful with the visual comparisons of constrained and unconstrained source maps displayed on the cortex surface, because they are very different types of data. In unconstrained source maps, we have '''three dipoles with orthogonal orientations at each cortex location''', therefore we cannot represent all the information at once. To display them as an activity map, Brainstorm computes the '''norm of the vectorial sum of the three orientations at each vertex'''. <<BR>>S = sqrt(Sx^2^ + Sy^2^ + Sz<<HTML(<SUP>2</SUP>)>>) <<BR>><<BR>> {{attachment:minnorm_unconstr_sketch.gif||height="158",width="476"}}
WARNING: The new interface presented here do not include all the options yet. The sLORETA normalization or the mixed head models are not supported: to use them, use the old interface (menu "Compute sources" instead of "Compute sources 2016", described in the old tutorials).
On the hard drive [TODO]
References [TODO]
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. Among the hundreds of methods available, two classes of inverse models have been widely used in MEG/EEG source imaging in the past years: minimum-norm solutions and beamformers.
Both approaches have the advantage of being linear: the activity of the sources is a linear recombination of the MEG/EEG recordings. It is possible to solve the inverse problem independently of the recordings, making the data manipulation a lot easier and faster.
This explains that we only observe positive values (no blue values even when the colormap is set to display positive and negative values): the norm displayed at each vertex is always positive. The underlying values along each orientation (x,y,z) can be positive or negative and oscillate around zero in time, but we cannot get access to this information with these static cortical maps.
Delete the unconstrained file, we will not explore more this option in the tutorials. You may refer to the advanced tutorial EEG and epilepsy for an example of analysis using unconstrained sources.
sLORETA: Produces a smoother maps where all the potentially activated area of the brain (given to the low spatial resolution of the source localization with MEG/EEG) is shown as connected, regardless of the depth of the sources. However, the units are difficult to interpret.
Run process "Standardize > Z-score normalization", baseline = [-100,-1.7]ms.
Do not select "Dynamic": This option offers a storage optimization that can save a lot of disk space for long time series, but which is not very relevant here (we use short epochs).
A value of 3 in this figure means: at this vertex, the value is 3 times higher than the standard deviation from zero during the baseline. If the values during the baseline follow a normal distribution N(μ)>,<2), then the values we computed follow a N(0,1)=Z distribution. We can infer a level of significance from this well know distribution, for instance a value Z=1.96 corresponds to a p-value of 0.05. These questions will be discussed in more details in the statistics tutorial.
The colormap used to display these Z-score maps is different than the other maps. It uses the colormap "Stat 1" instead of the colormap "Sources", and the color set is by default "hot" instead of "jet". You can redefine this: right-click on the figure > Colormap: Stat 1 > Colormap > jet.
Double-click on the colorbar to reset it to its default values.
The Z-normalized source maps are not impacted by the visualization filters. If you open simultaneously the time series and all the files you have now (MN, dSPM, sLORETA, Z-score) and modify the options in the Filter tab, all the figures are updated except for the Z-score. We can filter easily all the linear models (MN, dSPM, sLORETA), but we would lose the interesting properties of the Z-values if we were filtering them (the values would not follow a Z-distribution anymore).
Select all the normalized source maps (everything but the link) and delete them.
Select the options: Minimum norm imaging, Current density map, Unconstrained
Because we did not request to compute and inverse model for a specific block of recordings, it computed a shared inverse model. If you right-click on this new file, you get a warning message: "Inversion kernel". It does not contain any source map, but only the inverse operator that will allow us to convert the recordings in source maps.
The database explorer now shows one source link to this inverse model for each block of recordings available in this folder, single trials and averages. These links are not real files saved on the hard drive, but you can use them exactly like the first source file we calculated for the deviant average. If you load a link, Brainstorm loads the corresponding MEG recordings, loads the inverse kernel and multiplies the two on the fly before displaying it. This optimized approach saves a lot of computation time and lot of space on the hard drive.
Thanks to the linearity of the minimum norm model, the two following approaches are equivalent:
1) Averaging the sources of all the individual trials across runs,
2) Averaging the sources for the sensor averages that we already computed for each run. For non-standardized current density maps: MN(Average(trials)) = Average(MN(trials))
The second solution is a lot faster because it needs to read 4 files (one average file per run and per condition) instead of 456 files (total number of good trials in the two runs).
The file comments say "2 files" because they result in the averaging of two averages each (one from each run), but the number of corresponding trials is correctly updated in the file structure. Right-click on each of them > File > View file contents, and check the nAvg field:
Double-click on the source averages to display them (standard=top, deviant=bottom).
Use the predefined view "Left, Right" for looking at the two sides at once (shortcut: "7").
Select process "Standardize > Z-score normalization", baseline = [-100,-1.7]ms.
Do not run the computation, we will add the low-pass filter immediately after.
Add process "Pre-process > Band-pass filter": [0,40] Hz
The same SNR issues arise while averaging Z-scores: the average of the Z-scores is lower than the Z-score of the average. But this time we do not have an easy analytical expression to compensate the modifications in SNR, the average would not be reflecting the increase in SNR.
Always avoid averaging Z-score maps. Average the current density maps, then normalize.
A time stamp is added to the captured figure. The size of the text font is fixed, so if you want it to be readable in the contact sheet, you should make you figure very small before starting the capture.
One way to evaluate the accuracy of the source reconstruction if to use simulate recordings using the estimate source maps. This is done simply by multiplying the source time series with the forward model:
Then you can compare the original MEG recordings with the simulated ones visually. More formally, you can compute an error measure from the residuals (recordings - simulated).
Output mode [TODO]
This file contains the full time series (ImageGridAmp) instead of an inverse operator (ImagingKernel).
Forum: Spatial smoothing of sources: http://neuroimage.usc.edu/forums/showthread.php?1409
Forum: Units for dSPM and sLORETA: http://neuroimage.usc.edu/forums/showthread.php?1535
Forum: Sign of the MNE values: http://neuroimage.usc.edu/forums/showthread.php?1649#post7014
Forum: Combining magneto+gradiometers: http://neuroimage.usc.edu/forums/showthread.php?1900