low resolution brain electromagnetic tomography

yujian623

Scalp electric potentials (EEG) and extracranial magnetic fields (MEG) are due to the primary (impressed) current density distribution that arises from neuronal post-synaptic processes. A solution to the inverse problem, i.e. the computation of images of electric neuronal activity based on extracranial measurements, would provide important information on the time course and localization of brain function. In general, there is no unique solution to this problem. In particular, an instantaneous, distributed, discrete, linear solution capable of exact localization of point sources is of great interest, since the principles of linearity and superposition would guarantee its trustworthiness as a functional imaging method, given that brain activity occurs in the form of a finite number of distributed “hot spots”. Despite all previous efforts, linear solutions at best produced images with systematic non-zero localization errors. sLORETA yields images of standardized current density with zero localization error.


sLORETA is a method that computes images of electric neuronal activity from EEG and MEG. This software package is limited to EEG.
It is well known that EEG and MEG measurements do not contain enough information for the unique estimation of the electric neuronal generators. This means that many possible solutions exist, and sLORETA is nothing more than just one method among infinitely many.
Therefore, why should you use sLORETA? In my view, the only way to answer this question, scientifically (not politically), is to compare different methods, and to choose the best one. If one also prefers to be ethical about it, one must use the same “yardsticks” for all comparisons. The original sLORETA paper (Pascual-Marqui 2002) cited above contains many comparisons. The unique property of sLORETA is that, under ideal conditions, it localizes “test point sources” exactly. No other linear, distributed tomography shares this property. Furthermore, this property can be generalized to any source distribution, based on the principles of linearity and superposition.
For the sake of ethical science, one must also list the negative properties of a method. sLORETA has very low spatial resolution, and spatial resolution decreases with depth. Furthermore, sLORETA does not violate the law of conservation of garbage, i.e. “garbage in, garbage out”: if you feed sLORETA with noisy measurements, you will get noisy images. However, when comparing “negative” properties among all published linear, distributed tomographies, sLORETA remains the method of choice.



More detail and software download can be found:

http://www.unizh.ch/keyinst/NewLORETA/LORETA01.htm