Localization error in biomedical imaging

3D Statistical Parametric Mapping of EEG Source Spectra by Means of Variable Resolution Electromagnetic Tomography (VARETA)

This article describes a new method for 3D QEEG tomography in the frequency domain. A variant of Statistical Parametric Mapping is presented for source log spectra. Sources are estimated by means of a Discrete Spline EEG inverse solution known as Variable Resolution Electromagnetic Tomography (VARETA). Anatomical constraints are incorporated by the use of the Montreal Neurological Institute (MNI) probabilistic brain atlas. Efficient methods are developed for frequency domain VARETA in order to estimate the source spectra for the set of 103–105 voxels that comprise an EEG/MEG inverse solution. High resolution source Z spectra are then defined with respect to the age dependent mean and standard deviations of each voxel, which are summarized as regression equations calculated from the Cuban EEG normative database. The statistical issues involved are addressed by the use of extreme value statistics. Examples are shown that illustrate the potential clinical utility of the methods herein developed.

Multimodal quantitative neuroimaging databases and methods: the Cuban Human Brain Mapping Project

This article reviews the contributions of the Cuban Neuroscience Center to the evolution of the statistical parametric mapping (SPM) of quantitative Multimodal Neuroimages (qMN), from its inception to more recent work. Attention is limited to methods that compare individual qMN to normative databases (n/qMN). This evolution is described in three successive stages: (a) the development of one variant of normative topographical quantitative EEG (n/qEEG-top) which carries out statistical comparison of individual EEG spectral topographies with regard to a normative database--as part of the now popular SPM of brain descriptive parameters; (b) the development of n/qEEG tomography (n/qEEG-TOM), which employs brain electrical tomography (BET) to calculate voxelwise SPM maps of source spectral features with respect to a norm; (c) the development of a more general n/qMN by substituting EEG parameters with other neuroimaging descriptive parameters to obtain SPM maps. The study also describes the creation of Cuban normative databases, starting with the Cuban EEG database obtained in the early 90s, and more recently, the Cuban Human Brain Mapping Project (CHBMP). This project has created a 240 subject database of the normal Cuban population, obtained from a population-based random sample, comprising clinical, neuropsychological, EEG, MRI and SPECT data for the same subjects. Examples of clinical studies using qMN are given and, more importantly, receiver operator characteristics (ROC) analyses of the different developments document a sustained effort to assess the clinical usefulness of the techniques.

Bayesian M/EEG source reconstruction with spatio-temporal priors

This article proposes a Bayesian spatio-temporal model for source reconstruction of M/EEG data. The usual two-level probabilistic model implicit in most distributed source solutions is extended by adding a third level which describes the temporal evolution of neuronal current sources using time-domain General Linear Models (GLMs). These comprise a set of temporal basis functions which are used to describe event-related M/EEG responses. This places M/EEG analysis in a statistical framework that is very similar to that used for PET and fMRI. The experimental design can be coded in a design matrix, effects of interest characterized using contrasts and inferences made using posterior probability maps. Importantly, as is the case for single-subject fMRI analysis, trials are treated as fixed effects and the approach takes into account between-trial variance, allowing valid inferences to be made on single-subject data. The proposed probabilistic model is efficiently inverted by using the Variational Bayes framework under a convenient mean-field approximation (VB-GLM). The new method is tested with biophysically realistic simulated data and the results are compared to those obtained with traditional spatial approaches like the popular Low Resolution Electromagnetic TomogrAphy (LORETA) and minimum variance Beamformer. Finally, the VB-GLM approach is used to analyze an EEG data set from a face processing experiment.

Multivariate statistical brain electromagnetic mapping

Brain Electromagnetic Topography (BET) has attained widespread use. The representation of EEG or MEG parameters as scalp maps (BETm) aids its clinical interpretation. However, some critical issues limit the usefulness of BETm. In particular, the conventional statistical assessment of BETm with respect to normative data is based upon marginal significance probability scales which involve multiple univariate comparisons (one at each recording site). As a consequence, the probability of false positive findings (type I error) is increased above its nominal level. The use of conservative levels avoids this phenomenon but results in a considerable increase of the probability of not detecting real abnormality (type II error). Furthermore, BETm are constructed without taking into consideration the patterns of correlations characteristic of electromagnetic data under normal states of brain functioning. This limits the capability of BETm of representing multivariate aspects of abnormality. This paper introduces some techniques to approach these difficulties. Multivariate Brain Electromagnetic Topographic maps (MBETm) are defined, which retain the attractive features of mapping but also take advantage of multivariate characteristics (in the spatial and frequency domains) to highlight aspects of neuropathology. Moreover, simultaneous significance probability (SSP) scales, valid for both BETm and MBETm, are introduced for the global control of the probability of a type I error. The use of these techniques is illustrated with data from patients with cortical tumours and with epilepsy. ROC analysis shows that in some cases there is a significant improvement in both detection and localization accuracy.

High resolution quantitative EEG analysis

High resolution spectral methods are explored as an alternative to broad band spectral parameters (BBSP) in quantitative EEG analysis. In a previous paper (Valdes et al. 1990b) regression equations ("Developmental surfaces") were introduced to characterize the age-frequency distribution of the mean and standard deviation of the log spectral EEG power in a normative sample. These normative surfaces allow the calculation of z transformed spectra for all derivations of the 10/20 system and z maps for each frequency. Clinical material is presented that illustrates how these procedures may pinpoint frequencies of abnormal brain activity and their topographic distribution, avoiding the frequency and spatial "smearing" that may occur using BBSP. The increased diagnostic accuracy of high resolution spectral methods is demonstrated by means of receiver operator characteristic (ROC) curve analysis. Procedures are introduced to avoid type I error inflation due to the use of more variables in this type of procedure.