This article presents the improved version of the LORETA transform in the new version of BrainVision Analyzer 2.1.0 (to be released in the course of October 2014). The main features of the new LORETA dialog are explained and the virtues of LORETA analysis of EEG data are discussed. Hopefully this brief introduction will encourage you to use the new LORETA transform and push your research questions beyond the head surface into the neural source domain.
The fundamental discovery of the human electroencephalogram (EEG) by Hans Berger gave a new twist to the history of human neuroscience. After many decades of research and applications, EEG continues to be a very active and stimulating field of research. One of the main reasons is that EEG reflects the underlying dynamic symphony of neural activity at the millisecond scale, which offers a privileged window into brain function and the neural underpinnings of cognition. However, non-invasive human EEG recordings meet a natural frontier at the head surface, which prohibits direct access to the neural source domain. Therefore, true neural activity can only be inferred from the electrical potentials measured at each electrode site on the scalp. Source analysis techniques aim to estimate the location and dynamics of the underlying neural generators of EEG, i.e., to provide a solution to the EEG inverse problem (Grech et al., 2008). Although there is theoretically no unique solution to the EEG inverse problem, several techniques have been proposed to overcome this obstacle. Low Resolution Electromagnetic Tomography (LORETA) is one possible approach which computes an instantaneous, three-dimensional, discrete linear solution consisting of the smoothest of all possible neural current density distributions (Pascual-Marqui et al., 1994). LORETA is a well-established method which has gained wide-spread popularity and has been used on hundreds of scientific publications (Pascual-Marqui et al., 2002).
In Analyzer, the LORETA transform computes the full three-dimensional current density distribution (the current density field). However, the data output consists of average current density within predefined locations or Regions of Interest (ROIs) in the source space. Additionally, the LORETA transient view provides a qualitative analysis and visualization of the 3D distribution of neural current density.
In Analyzer 2.1.0 the LORETA dialog has been considerably improved in order to facilitate the creation of ROIs and their visualization in the MNI brain images.
Several new features are worth mentioning:
- ROIs are inserted as nodes in a tree structure (ROI tree). Brain regions that are included as part of a ROI appear as child nodes of the given ROI node (Figure 1).
- Each node in the tree can be selectively visualized as a red area in the MNI brain images.
- The sequence of LORETA channels created by the LORETA transform can be easily set by specifying the ROI sequence in the tree with the options Move Up and Move Down.
- Brain regions can be comfortably added to a ROI by inserting predefined anatomical areas (Lobes, Gyri and Brodmann Areas) and other arbitrary cubic, spherical or single-voxel areas.
- ROIs can also be defined using the option Export MNI Voxels File. The exported *.csv file can be edited to add a set of voxels as specified by other neuroimaging techniques such as fMRI, PET, etc. to a given ROI. In other words, ROIs can be conveniently specified on the basis of data obtained with other neuroimaging techniques.
- The tree structure can be easily edited by using the context menu of a node. The context menu offers several options to expand and collapse a given portion of the tree, rename and delete nodes, and update brain regions.
- The output data in each LORETA channel can be defined as one specific vector component (X, Y or Z), the vector magnitude or the squared vector magnitude. Please note that the vector magnitude always has positive values, while a component may have both positive and negative values depending on the 3D orientation of the current density vector.
Figure 1: LORETA tree – ROIs located in the visual cortex.
Dialog of the LORETA transform. Six ROIs in primary visual cortex (Brodmann Area 17 right and left) and the extrastriate visual areas (Brodmann Areas 18 and 19 right and left) are inserted in the ROI tree. The ROI BA17L (Brodmann Area 17 left) is partially depicted in the MNI brain images.
Once ROIs are fully specified, the average current density for each ROI is shown in an associated LORETA channel which depicts the neural activation dynamics. This means that there is a dedicated LORETA channel associated with each defined ROI (Figure 2).
Figure 2: LORETA analysis of the Visual Evoked Potential (VEP)
Left panel: Butterfly view of VEP data. LORETA transient view within the time interval [90 ms – 109 ms] around the P100 peak of the VEP. Right panel: Average current density for the corresponding LORETA channels.
As shown in Figure 1, it is now very easy to create a ROI tree where the spatial location of each contributing brain region can be visualized in the MNI brain images. Besides, EEG/ERP data can be combined with the LORETA transient view and the LORETA channels in order to create a comprehensive picture of the underlying neural dynamics.
In case you have experienced some obstacles in the past to perform a source analysis of your time-domain data, why not try it now in the new version of Analyzer 2.1.0? I would like to encourage you to get familiar with this technique and use it in your own research. Incorporating LORETA analysis into your EEG processing pipeline may also increase the impact of your publications. My colleagues in the Scientific Support team and I will be happy to help you through the necessary steps to enhance your expertise in the field of EEG source analysis. Do not hesitate to contact us at email@example.com for more details.