Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

The event-related potential (ERP) technique in cognitive neuroscience allows scientists to observe human brain activity that reflects specific cognitive processes. In An Introduction to the Event-Related Potential Technique, Steve Luck offers the first comprehensive guide to the practicalities of conducting ERP experiments in cognitive neuroscience and related fields, including affective neuroscience and experimental psychopathology. The book can serve as a guide for the classroom or the laboratory and as a reference for researchers who do not conduct ERP studies themselves but need to understand and evaluate ERP experiments in the literature. It summarizes the accumulated body of ERP theory and practice, providing detailed, practical advice about how to design, conduct, and interpret ERP experiments, and presents the theoretical background needed to understand why an experiment is carried out in a particular way. Luck focuses on the most fundamental techniques, describing them as they are used in many of the world's leading ERP laboratories. These techniques reflect a long history of electrophysiological recordings and provide an excellent foundation for more advanced approaches. The book also provides advice on the key topic of how to design ERP experiments so that they will be useful in answering questions of broad scientific interest. This reflects the increasing proportion of ERP research that focuses on these broader questions rather than the "ERPology" of early studies, which concentrated primarily on ERP components and methods. Topics covered include the neural origins of ERPs, signal averaging, artifact rejection and correction, filtering, measurement and analysis, localization, and the practicalities of setting up the lab.

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An Introduction to the Event-Related Potential Technique

Validity of the executive function theory of attention-deficit/hyperactivity disorder: a meta-analytic review.

Scalp electric potentials (electroencephalograms) and extracranial magnetic fields (magnetoencephalograms) are due to the primary (impressed) current density distribution that arises from neuronal postsynaptic processes. A solution to the inverse problem--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 nonzero localization errors. A solution reported here yields images of standardized current density with zero localization error. The purpose of this paper is to present the technical details of the method, allowing researchers to test, check, reproduce and validate the new method.

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Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.

Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

A brain microstate is defined as a functional/physiological state of the brain during which specific neural computations are performed. It is characterized uniquely by a fixed spatial distribution of active neuronal generators with time varying intensity. Brain electrical activity is modeled as being composed of a time sequence of nonoverlapping microstates with variable duration. A precise mathematical formulation of the model for evoked potential recordings is presented, where the microstates are represented as normalized vectors constituted by scalp electric potentials due to the underlying generators. An algorithm is developed for estimating the microstates, based on a modified version of the classical k-means clustering method, in which cluster orientations are estimated, Consequently, each instantaneous multichannel evoked potential measurement is classified as belonging to some microstate, thus producing a natural segmentation of brain activity. Use is made of statistical image segmentation techniques for obtaining smooth continuous segments. Time varying intensities are estimated by projecting the measurements onto their corresponding microstates. A goodness of fit statistic for the model is presented. Finally, a method is introduced for estimating the number of microstates, based on nonparametric data-driven statistical resampling techniques. >

Segmentation of brain electrical activity into microstates: model estimation and validation

This paper reviews the class of instantaneous, 3D, discrete, linear solutions for the EEG inverse problem. Five different inverse methods are analyzed and compared: minimum norm, weighted minimum norm, Backus and Gilbert, weighted resolution optimization (WROP), and low resolution brain electromagnetic tomography (LORETA). The inverse methods are compared by testing localization errors in the estimation of single and multiple sources. These tests constitute the minimum necessary condition to be satisfied by any tomography. Of the five inverse solutions tested, only LORETA demonstrates the ability of correct localization in 3D space. The other four inverse solutions should not be used if the research aim is to localize the neuronal generators of EEG in a 3D brain. In this sense, minimum norm, weighted minimum norm, Backus and Gilbert, and WROP can be likened to x-rays, where depth information is totally lacking. For the sake of reproducible research, all the material and methods used in this part of the study, consisting of computer programs (source code and executables) and data, are available upon request to the author. In this way, all the results and conclusions can be checked, reproduced, and validated by the interested reader. In the final part of this paper, LORETA in the standard Talairach human brain is presented. This technique allows the quantitative neuroanatomical localization of neuronal electric activity. A computer program for LORETA in Talairach space is available upon request from the author. 1. Localization properties of instantaneous, 3D, discrete, linear solutions for the EEG inverse problem One of the primary concerns in electrophysiology is the non-invasive localization of the neuronal generators responsible for measured EEG phenomena. Methods for localization are termed inverse solutions. This review is limited to the class of instantaneous, 3D, discrete, linear solutions for the EEG inverse problem. In order for an inverse solution of this class to qualify as a true functional “tomography”, it must at least be capable of localizing sources with a minimum of localization error. If an inverse solution of this class is incapable of correct localization, then it has no worth as a tomography. Harsh as this criterion may seem, it is fair and objective, but most important of all, it is applicable to any proposed method. The main difficulty impeding the development of a “good” tomography for the generators of the EEG is determined by the physics nature of the problem: the measurements do not contain enough information about the generators. This gives rise to what is known as the non-uniqueness of the inverse solution. Therefore, from the outset, it can be stated that a perfect tomography can not exist. R.D. Pascual-Marqui. Review of Methods for Solving the EEG Inverse Problem. International Journal of Bioelectromagnetism 1999, Volume 1, Number 1, pp:75-86. Printed Issue ISSN 1457-7857, Internet Issue ISSN 14567865 (http://www.tut.fi/ijbem). Author’s version. Page 2 of 13 Despite this obstacle, the search for better tomographies goes on, as witnessed by the number of papers being published in this field (see, e.g., Koles (1998) for a recent review). From a more optimistic point of view, one might expect that whatever little information is contained in EEG measurements, it should suffice to allow for the existence of at least an “approximate” tomography. Such a tomography should be capable of recovering the “true” generators with an acceptable low level of distortion (i.e., of error). Historically, the first tomography published in this field was the minimum norm inverse solution of Hamalainen and Ilmoniemi (1984). The properties of this method for 2D solution spaces (i.e., sources restricted to a plane or to a spherical surface running parallel to the measurement surface) were promising. Two-dimensional images of estimated current density corresponding to ideal point sources were recovered with blurring, but with correct localization of activity maxima. However, this method is incapable of correct localization in 3D solution spaces, as was shown in PascualMarqui (1995). The greatest challenge in the development of EEG source localization tomographies is to extend the good localization properties of the 2D minimum norm solution to 3D solution spaces. This was achieved with LORETA (low resolution brain electromagnetic tomography) (Pascual-Marqui et al., 1994; Pascual-Marqui, 1995). All the properties of a tomography, including its quality in terms of localization capability, can be completely characterized by means of the model resolution matrix (Menke, 1984; Backus and Gilbert, 1968). This approach was used by Pascual-Marqui (1995) to compare three tomographies (inverse solutions) in terms of their localization errors. The first part of this paper contains a brief review of the theory of instantaneous, 3D, discrete, linear solutions for the EEG inverse problem. A methodology is presented for the fair, objective, and rigorous comparison of EEG-based tomographies. The main results presented here correspond to a comparison of five different tomographies taken from the published literature. Some important aspects of inverse solutions not included in this study, such as the effect of noisy measurements and the effect of the reference electrode for EEG measurements, were considered in detail elsewhere (Pascual-Marqui, 1995). Other methods of source localization, such as single or multiple dipole fitting are not the object of this review. 1.1 Material and methods

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Review of methods for solving the EEG inverse problem

This paper reviews several recent publications that have successfully used the functional brain imaging method known as LORETA. Emphasis is placed on the electrophysiological and neuroanatomical basis of the method, on the localization properties of the method, and on the validation of the method in real experimental human data. Papers that criticize LORETA are briefly discussed. LORETA publications in the 1994-1997 period based localization inference on images of raw electric neuronal activity. In 1998, a series of papers appeared that based localization inference on the statistical parametric mapping methodology applied to high-time resolution LORETA images. Starting in 1999, quantitative neuroanatomy was added to the methodology, based on the digitized Talairach atlas provided by the Brain Imaging Centre, Montreal Neurological Institute. The combination of these methodological developments has placed LORETA at a level that compares favorably to the more classical functional imaging methods, such as PET and fMRI.

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Roberto D. Pascual-Marqui

Papers

Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details.

Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

Segmentation of brain electrical activity into microstates: model estimation and validation

Review of methods for solving the EEG inverse problem

Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review.