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tutorial:natmeg:beamforming [2018/10/21 15:08]
42.49.180.224 [Beamforming oscillatory responses in combined MEG/EEG data]
tutorial:natmeg:beamforming [2018/03/30 09:23] (current)
222.29.101.70 [Identifying a time window of interest]
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 The brain is divided in a regular three dimensional grid and the source strength for each grid point is computed. The method applied in this example is termed Dynamical Imaging of Coherent Sources (DICS) and the estimates are calculated in the frequency domain (Gross et al. 2001). Other beamformer methods rely on sources estimates calculated in the time domain, e.g. the Linearly Constrained Minimum Variance (LCMV) and Synthetic Aperture Magnetometry (SAM) methods (van Veen et al., 1997; Robinson and Cheyne, 1997). These methods produce a 3D spatial distribution of the power of the neuronal sources. This distribution is then overlaid on a structural image of the subject'​s brain. Furthermore,​ these distributions of source power can be subjected to statistical analysis. It is always ideal to contrast the activity of interest against some control/​baseline activity. Options for this will be discussed below, but it is best to keep this in mind when designing your experiment from the start, rather than struggle to find a suitable control/​baseline after data collection. The brain is divided in a regular three dimensional grid and the source strength for each grid point is computed. The method applied in this example is termed Dynamical Imaging of Coherent Sources (DICS) and the estimates are calculated in the frequency domain (Gross et al. 2001). Other beamformer methods rely on sources estimates calculated in the time domain, e.g. the Linearly Constrained Minimum Variance (LCMV) and Synthetic Aperture Magnetometry (SAM) methods (van Veen et al., 1997; Robinson and Cheyne, 1997). These methods produce a 3D spatial distribution of the power of the neuronal sources. This distribution is then overlaid on a structural image of the subject'​s brain. Furthermore,​ these distributions of source power can be subjected to statistical analysis. It is always ideal to contrast the activity of interest against some control/​baseline activity. Options for this will be discussed below, but it is best to keep this in mind when designing your experiment from the start, rather than struggle to find a suitable control/​baseline after data collection.
  
-====== Beamforming oscillatory responses in combined MEG/EEG data ======+===== Procedure ​===== 
 + 
 +    
 +To localize the oscillatory sources for the example dataset we will perform the following steps: 
 + 
 +  * Reading in the subject specific anatomical MRI using **[[reference:​ft_read_mri|ft_read_mri]]**  
 +  * Construct a forward model using **[[reference:​ft_volumesegment|ft_volumesegment]]** and **[[reference:​ft_prepare_headmodel|ft_prepare_headmodel]]**  
 +  * Prepare the source model using **[[reference:​ft_prepare_sourcemodel|ft_prepare_sourcemodel]]** 
 + 
 +Next, we head out to investigate the response to the finger movement. We will localize the sources of the motor beta-band activity following the following steps: 
 + 
 +  * Load the data from disk and define baseline and poststimulus period using **[[reference:​ft_redefinetrial|ft_redefinetrial]]**  
 +  * Compute the cross-spectral density matrix for all MEG channels using the function **[[reference:​ft_freqanalysis|ft_freqanalysis]]** 
 +  * Compute the lead field matrices using **[[reference:​ft_prepare_leadfield|ft_prepare_leadfield]]** 
 +  * Compute a common spatial filter and estimate the power of the sources using **[[reference:​ft_sourceanalysis|ft_sourceanalysis]]** 
 +  * Compute the condition difference 
 +  * Visualize the result with **[[reference:​ft_sourceplot|ft_sourceplot]]** 
 + 
 +Note that some of the steps will be skipped in this tutorial as we have already done them in the previous days of the workshop. 
 + 
 +{{:​tutorial:​bf_pipeline.jpg?​direct&​650|}} 
 + 
 +//Figure 1; An example of a pipeline to locate oscillatory sources.//​ 
 ===== Preparing the data and the forward and inverse model ===== ===== Preparing the data and the forward and inverse model =====