We have obtained Dr. Leslie Sherlin's permision to publish his introduction for Neuroconnections Issue (Winter 2010), dedicated to
LORETA neuroimaging method of EEG source localisation.
In this article Leslie explains its significance for brain diagnostics and support for decisions in Neurofeedback training.
"I am very pleased to be able to contribute to the NeuroConnections effort in coalescing articles on the topic of LORETA. This analysis procedure has permeated our organizations and those professionals who are utilizing tools the field of quantitative electroencephalography (QEEG) for the purposes education, research and guiding interventions such as neurofeedback. I feel tremendous fortune to have been a part of this history in a small way by observing this growth within our community and to have contributed in some minor way to the use of LORETA in this capacity. There are many excellent writings both formal and less formal that describe the methods of LORETA. My intention isn’t to attempt to explain how this modality is calculated nor present to you the vast body of research of various populations of individuals but instead to summarize from a historical point of view the use of LORETA within our specific community.
I’ll hope to achieve this goal in an informal way by telling the story of the implementation of these methods from my perspective. For a bit more formal description of the use of LORETA families and some examples you can see chapter 4 authored by myself (Sherlin, 2009) in the second edition of the book Introduction to Quantitative EEG and Neurofeedback edited Budzynski, Budzynski, Evans & Arbarbanel. For the most formal and thorough listing of the LORETA techniques and it’s technical development I refer you to the author of the method Roberto Pasucal-Marqui, PhD and the website of the Key Institute for Brain-Mind Research (http://www.uzh.ch/keyinst/loreta.htm).
As many of you know I was lucky early in my college career by growing up and attending the state university, The University of Tennessee in Knoxville. This afforded me the serendipitous opportunity to end up in the class and in the laboratory of Joel Lubar, PhD as an undergraduate student. In 2000 I had the great privilege to visit with Roberto Pascual-Marqui PhD, the developer of the LORETA inverse solution, with my colleague and fellow student Marco Congedo. At this time the LORETA-Key software (Pascual-Marqui, 1994, 1999), had not been widely distributed and utilized in the United States. Marco had significant interest in using LORETA for visualizing brain activity and for exploring newer methods for neurofeedback and had many questions for Roberto. Interestingly Marco was so persistent finally Roberto refused any more electronic questions but made the invitation to visit to find the answers. So upon the invitation of Roberto, Marco found funding to travel to Zurich and learn the details and I happen to be standing in the right spot at the right time. I somehow knew this was an opportunity not to be missed. Roberto trained us extensively on how to use his software, named LORETA-Key, which had been already released as free academic software. The LORETA-Key software is a collection of independent modules that the user must run in sequence in order to get from raw EEG to LORETA images.
LORETA is a specific solution to the inverse problem. The method was originally described in 1994 by Pascual-Marqui, Michel and Lehman (Pascual-Marqui et al., 1994)however it had been under development for years.This first paper presented the LORETA method as a new method for localizing the electric activity in the brain based on scalp potentials from multiple channel EEG recordings. This model was in contrast to previously described models because it did not require a limited number of point sources or a known surface distribution. Instead it computes the distribution of the current source density (CSD), measured in microamperes, through the full brain volume (Sherlin, 2009).
The LORETA analysis is unlike other quantitative EEG analysis techniques because it is capable of determining the relative activity of regions in the brain using surface electrodes (Pascual-Marqui, 1999). The EEG is a measure of electrical potential differences but the LORETA method estimates current densities at a deeper cortical level.
Upon our return after learning the mechanics of performing an analysis, which was very labor intensive with many batch steps running many different modules, I wanted us to develop a macro of sorts that would perform the extensive steps necessary in the LORETA-Key software all at once taking care of all possible options in a clear and understandable manner. The room for error was great and the user had to be tedious and detailed and still the process would take significant time. With some already existing programming skills Marco wrote a very clean and basic program that would run the LORETA-Key modules after the user had input the necessary data details. This program we called the Workstation (Congedo, 2000). Upon the recommendation of our professor and supervisor Joel Lubar, PhD, we formed a company to distribute this program to the larger clinical field of neurofeedback and QEEG providers. This very straightforward program was the first 3rd party software utilizing the LORETA-Key and was well received because it allowed clinically oriented users the ability to perform LORETA analysis with greater ease and the ability to utilize this incredible tool in performing and understanding the current source density localization in their clients. Later there were several other software tools developed that were aimed at the neurofeedback community. Since 2005, all of these software have been released as freeware, following Roberto’s lead, stating unequivocally that our goal was to make inverse solution tools as accessible as possible in the neurofeedback community.
Newer methods were developed by Pascual-Maqui in 2002 and it was named standardized LORETA or sLORETA (Pascual Marqui, 2002). This new implementation had to its advantage the ability to localize test point sources with zero localization error in the absence of noise, which had not previously been accomplished. Since my goal here is not to distinguish the difference in the methods I will skip over these technical issues. It will suffice to say that despite the name, from a mathematical point of view sLORETA is very different from the old LORETA method, and much more accurate. The most recent release and development of this family of inverse solutions is Exact Low Resolution Brain Electromagnetic Tomography (eLORETA). eLORETA is not a linear imaging method but is a true inverse solution with exact and zero localization errors (Pascual Marqui, 2007a, Pascual Marqui et al., 2006). Since 2008 the sLORETA/eLORETA software has included analysis of microstates, independent component analysis (ICA) and additionally contained the Brain Research Laboratories (BRL) normative comparison capability, still as freeware. “The norms, the software, and their validation have been developed in collaboration with E. Roy John, Leslie Prichep, and Roberto Isenhart, from the Brain Research Laboratories (BRL), Department of Psychiatry, NY University School of Medicine, NY, USA” (Pascual-Marqui, 2010). The full details and technical specifications of each of the aforementioned methods can again be found at the website of Roberto Pascual-Marqui and the KEY Institute for Mind-Brain Research (Pascual Marqui, 2007b) and in the cited publications.
It wasn’t long after a wider spread distribution of the LORETA software and use in the community of QEEG and neurofeedback that the natural question arose, “if the current source density of interior cortical areas could be operant conditioned in the same manner as the scalp neurofeedback was being conducted currently?” This was actually our first concern as announced during a workshop at ISNR by Joel Lubar, Marco Congedo, David Joffe and myself (Lubar, Congedo, Joffe, & Sherlin, 2001). The workshop was the starting point for a 3-year project that would be Marco’s dissertation where he demonstrated and verified that in fact the deeper structures could be trained using “LORETA feedback” (Congedo, 2003). This work was published the following year in IEEE Trans. in Rehabilitation Engineering and Neuronal systems (Congedo, Lubar and Joffe, 2004) and is still today a pioneering study in multi-channel neurofeedback. Dr. Lubar’s lab, and in particular Rex Cannon, continued to pursue these techniques with additional validation studies (Cannon et al, 2007, 2009). Currently the method is used in several other universities and is becoming available in several neurofeedback systems offered to clinicians as an experimental module. I’ve been lucky to be involved in a number of these experimental clinical studies with the most recent very exciting work taking place at the University of Alberta in Canada by the principle author Douglas Ozier in the area of chronic pain utilizing the updated algorithm of sLORETA for feedback with exact localization. Ozier has contributed to this issue as well so I’ll direct you to his contribution for further details with publications soon to follow.
It’s been fun and interesting to see the past ten years go by with the advances both in the technique but also to see the new applications and uses of LORETA. It was only a short time ago that this method was being developed and now it is practically understood and a familiar part of most QEEG analysis. It is utilized by many of our colleagues in research in attempt to understand variability in populations and has a real promise of impacting the way that we provide neurofeedback. As a final comment I’ll raise your awareness that many developments are continuing in the area of connectivity between cortical areas using LORETA techniques. You might start looking forward to learning more about this particularly interesting aspect at the 2011 conference where Roberto Pascual-Marqui, a lifetime achievement award recipient, is planned to be a Keynote speaker.
References
Cannon R., Congedo M., Lubar J.F., Hutchens T. (2009). Differentiating a network of
executive attention: LORETA neurofeedback in anterior cingulate and dorsolateral prefrontal cortices. International Journal of Neuroscience, 119, 404-441.Cannon R., Lubar J.F., Congedo M., Thornton K., Towler K., Hutchens T. (2007), The
Effect of Neurofeedback Training in the Cognitive Division of the Anterior Cingulate Gyrus, International Journal of Neuroscience, 117(3), 337-57.Congedo, M. (2000). Workstation (Version 1.0). Knoxville, TN: Nova Tech EEG, Inc.
Congedo, M. (2001). EEG Editor (Version 1.0). Knoxville, TN: Nova Tech EEG, Inc.
Congedo, M. (2003). Tomographic Neurofeedback; a new Technique for the
Self-Regulation of Brain Electrical Activity. University of Tennessee, Knoxville.Congedo M., Lubar J.F. (2003), Parametric and Non-Parametric Normative Database
Comparisons in Electroencephalography: A Simulation Study on Accuracy, Journal of Neurotherapy, 7(3/4), 1-29.Congedo, M. (2004). sLORETA zero-localization error as seen in a point spread functions: an animation Retrieved April 29, 2009, from http://www.lis.inpg.fr/pages_perso/congedo/sLORETA.htm
Congedo M., Lubar J.F., Joffe D. (2004), Low-Resolution Electromagnetic
Tomography neurofeedback, IEEE Trans. on Neuronal Systems & Rehabilitation Engineering, 12(4), 387-397.Congedo, M. (2005). EureKa! (Version 3.0). Mesa, AZ: Nova Tech EEG, Inc.
Congedo M., Lotte F, Lécuyer A. (2006), Classification of Movement Intention by
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Cannon R., Congedo M., Lubar J.F., Hutchens T. (2009). Differentiating a network of
executive attention: LORETA neurofeedback in anterior cingulate and dorsolateral prefrontal cortices.International Journal of Neuroscience, 119, 404-441.
Cannon R., Lubar J.F., Congedo M., Thornton K., Towler K., Hutchens T. (2007), The
Effect of Neurofeedback Training in the Cognitive Division of the Anterior Cingulate Gyrus, International Journal of Neuroscience, 117(3), 337-57.
Electromagnetic Imaging, IEEE Transactions on Biomedical Engineering, 53(8), 1624-34.
Congedo M., Joffe D. (2007), Multi-Channel Spatial Filters for Neurofeedback. In
“Neurofeedback: Dynamics and Clinical Applications “, (Ed) Evans J., Haworth Press, New York,Lubar, J. F., Congedo, M., Joffe, D., & Sherlin, L. (2001). LORETA 3-D Neurofeedback, Normative Database and New Findings. Paper presented at the Society for Neuronal Regulation.
Ozier, D., Whelton, W., Mueller, H., Lampman, D., & Sherlin, L. (unpublished). Comparing the efficacy of thermal biofeedback and sLORETA neurotherapy as interventions for chronic pain., University of Alberta, Edmonton.
Pascual-Marqui RD, Michel CM, Lehmann D. (1994). Low resolution electromagnetic
tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18:49-65.Pascual-Marqui RD. (1999). Review of Methods for Solving the EEG Inverse
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tomography (sLORETA): technical details, Methods Find. Experimental Clinical Pharmacology, 24(D), 5-12.Pascual-Marqui, R.D., Esslen, M., Kochi, k., and Lehmann,D. (2002b). Functional
imaging with low resolution brain electromagnetic tomography (LORETA): A review. Meth. Findings Exp. Clin. Pharmacol., vol. 24C, pp. 91–95.Pascual-Marqui, R.D., Esslen, M., Kochi, k., and Lehmann,D. (2002c). Functional
imaging with low resolution brain electromagnetic tomography (LORETA): Review, new comparisons, and new validation. Jpn. J. Clin. Neurophysiol., vol. 30, pp. 81–94.Pascual-Marqui, R. D. (2007a) Discrete 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization. . arXiv:0710.3341 [math-ph].
Pascual-Marqui, R. D. (2007b) LORETA: low resolution brain electromagnetic tomography. Zurich, Switzerland, The KEY Institute for Brain-Mind Research.
Pascual-Marqui, R.D. (2010). Standardized & Exact low resolution brain electromagnetic tomography. Retrieved from, http://www.uzh.ch/keyinst/loreta.htm on November 6, 2010.
Sherlin, L. (2009). Diagnosing and Treating Brain Function through the use of Low
Resolution Electromagnetic Tomography (LORETA). In T. Budzynski, H. K. Budzynski, J. Evans & A. Abarbanel (Eds.), Introduction to Quantitative EEG and Neurofeedback, Advanced Theory and Applications (2 ed.): Elsevier.Wagner, M., Fuchs, M., Kastner, J. (2004). Evaluation of sLORETA in the presence of noise and multiple sources,”. Brain Topogr., vol. 16, no. 4, pp. 277-280 ".