Brainstorm vs BESA/ dipole fitting vs MNI+sLORETA

Hi all,

I've been very confused with the concepts, trying to show where Brainstorm and BESA could differ in resting state EEG data. In BESA a method was implemented using dipole fitting (81 Virtual 10-10 montage) averaged to 15 predefined locations. Currently, I am using Brainstorm to localize using minimum norm imaging and sLORETA and then extracting scout time series from a reduced Desikan Killiany atlas(10 regions). I was wondering how different would the signals be. My thinking is that the BESA dipfit only calculates activity over the 15 dipoles whereas Brainstorm calculates activity over 25000 distributed dipoles, localizing activity by minimizing errors. I would appreciate it if someone could direct me to papers comparing BESA and Brainstorm over resting state activity and the explanation as to why the MNI+sLORETA is so different from dip fit in BESA.

Secondly, the source level signal amplitudes are in the order of pAm in Brainstorm while those from BESA are nAm. What is the possible reason for this? What procedure does brainstorm do that makes the amplitudes so less while localizing?

I hope someone can help me with the explanations of these questions.


Hi @a13

We appology for the late reply on this topic. I'm not familiar with BESA.

The difference in both software can be explained by the different methods and parameters used to compute the forward and the inverse solution.

In Brainstorm, you can define the amount of dipoles and their positions that you want in your model.
I'm not aware of any publication that compared the two software.

From my point of view, the pAm as units for the dipoles are more realistic.
(maybe since BESA use fewer # dipoles, then the activation is not for a unit dipole, so probably the unit reflect a larger group of dipole, and not a unit dipole)