Brain connectivity for constructing new face representations in typical adults versus a prosopagnosic patient.

Will our brains get to know a new face better if we look at its external features first? Here we offer neurophysiological evidence of the relevance of external versus internal facial features for constructing new face representations, by contrasting successful face processing with a prototypical case of face agnosia. A woman with acquired prosopagnosia (E.C.

EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance.

Introduction: The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described.

Methods: Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep.

Procedures: The EEG primary currents at the source were described with the sLoreta method.

Resting EEG effective connectivity at the sources in developmental dysphonetic dyslexia. Differences with non-specific reading delay.

Previous studies conducted on subjects with dysphonetic dyslexia (DD) reported inefficient timing integration of information from various brain areas. This dysregulation has been referred as neuronal dyschronia or timing deficiency. The present study examines the effective brain connectivity in Dysphonetic Dyslexic subjects (DD) compared to a group of subjects with non-specific reading delay (NSRD).

Unmixing EEG Inverse Solutions Based on Brain Segmentation.

Due to its low resolution, any EEG inverse solution provides a source estimate at each voxel that is a mixture of the true source values over all the voxels of the brain. This mixing effect usually causes notable distortion in estimates of source connectivity based on inverse solutions. To lessen this shortcoming, an unmixing approach is introduced for EEG inverse solutions based on piecewise approximation of the unknown source by means of a brain segmentation formed by specified Regions of Interests (ROIs).

Assessing direct paths of intracortical causal information flow of oscillatory activity with the isolated effective coherence (iCoh).

Functional connectivity is of central importance in understanding brain function. For this purpose, multiple time series of electric cortical activity can be used for assessing the properties of a network: the strength, directionality, and spectral characteristics (i.e.

A maximum linear separation criterion for the analysis of neurophysiological data.

In this paper we propose an approach for the extraction of features that differentiate two populations or two experimental conditions in a neurophysiological experiment. These features consist of summarizing variables defined as total activity (e.g.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology--specifically, morphological erosions and dilations--that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

Morphology-based hypothesis testing in discrete random fields: a non-parametric method to address the multiple-comparison problem in neuroimaging.

A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level.

Support vector regression for functional data in multivariate calibration problems.

Quantitative analyses involving instrumental signals, such as chromatograms, NIR, and MIR spectra have been successfully applied nowadays for the solution of important chemical tasks. Multivariate calibration is very useful for such purposes and the commonly used methods in chemometrics consider each sample spectrum as a sequence of discrete data points. An alternative way to analyze spectral data is to consider each sample as a function, in which a functional data is obtained.

A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering.

We present a new approach for estimating solutions of the dynamical inverse problem of EEG generation. In contrast to previous approaches, we reinterpret this problem as a filtering problem in a state space framework; for the purpose of its solution, we propose a new extension of Kalman filtering to the case of spatiotemporal dynamics. The temporal evolution of the distributed generators of the EEG can be reconstructed at each voxel of a discretisation of the gray matter of brain.

Random field-union intersection tests for EEG/MEG imaging.

Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same spatiotemporal data: topographic and tomographic. Until now, statistical tests for these two situations have developed separately. This work introduces statistical tests for assessing simultaneously the significance of spatiotemporal event-related potential/event-related field (ERP/ERF) components and that of their sources.

Recursive penalized least squares solution for dynamical inverse problems of EEG generation.

In the dynamical inverse problem of electroencephalogram (EEG) generation where a specific dynamics for the electrical current distribution is assumed, we can impose general spatiotemporal constraints onto the solution by casting the problem into a state space representation and assuming a specific class of parametric models for the dynamics. The Akaike Bayesian Information Criterion (ABIC), which is based on the Type II likelihood, was used to estimate the parameters and evaluate the model. In addition, dynamic low-resolution brain electromagnetic tomography (LORETA), a new approach for estimating the current distribution is introduced.

Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex.

Information processing in the visual cortex depends on complex and context sensitive patterns of interactions between neuronal groups in many different cortical areas. Methods used to date for disentangling this functional connectivity presuppose either linearity or instantaneous interactions, assumptions that are not necessarily valid. In this paper a general framework that encompasses both linear and non-linear modelling of neurophysiological time series data by means of Local Linear Non-linear Autoregressive models (LLNAR) is described.

Nonlinear EEG analysis based on a neural mass model.

The well-known neural mass model described by Lopes da Silva et al. (1976) and Zetterberg et al. (1978) is fitted to actual EEG data.

Comparison of DNA migrations in two clamped homogeneous electric field chambers of different sizes. Relation between sample thickness and electrophoresis time.

We present here a method to compare the mathematical descriptions of DNA migration per pulse as a function of pulse time. It is based on obtaining robust estimates and variances of DNA reorientation time, migration velocities during and after DNA reorientation; and on the statistical comparisons of these estimates. We demonstrated an equal description for the migration per pulse of each DNA molecule separated under identical conditions in clamped homogeneous electric field (CHEF) and miniCHEF chambers.

Reparametrization of autoregressive models with coefficients depending on covariables: application to EEG spectrum maturation.

To describe the spectral characteristics of the EGG development through autoregressive (AR) time series models it is necessary to perform regression analysis of the AR parameters with regards to the age of the subject. A major difficulty in this approach is the very complex nature of the admissible region of the AR coefficients, which impedes the straight use of regression techniques. The present paper overcomes this difficulty by first applying the Barndoff-Nielsen and Schou reparametrization of AR models, followed by Fisher's transformation and then carrying out age regression analysis of the transformed parameters.

Testing topographic differences between event related brain potentials by using non-parametric combinations of permutation tests.

MANOVA and repeated measures ANOVA approaches have provided evidence of a number of limitations in several event-related potential (ERP) studies due to violations of their statistical assumptions and the typically moderate size of the available sample. Alternative, computer-intensive methods based on permutation principles have recently been developed. Up to now this methodology has focused mostly on magnitude differences between scalp distributions as measured by t statistics.

Multiresolution decomposition of non-stationary EEG signals: a preliminary study.

Wavelet representation is a recent development in the analysis of non-stationary signals. Its possibilities for use in the description of time-frequency characteristics of both transients in spontaneous EEG and time-varying rhythms in event related brain activity are explored here. By way of illustration, multiresolution decompositions of a wide variety of EEG transients are carried out in this work, including spike-and-waves, single spikes, sharp waves, blink artifacts, frontal intermittent rhythmic delta activity (FIRDA) and paroxysmal delta activity.

EEG predictability: adequacy of non-linear forecasting methods.

The predictive properties of EEG segments were analyzed. The sample included alpha, delta as well as spike and wave EEG activity recordings. Most of these segments are better described with non-linear autoregressive models, and a non-linear forecasting algorithm is routinely required.

Maximum a posteriori estimation of change points in the EEG.

A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels.

Measuring the dissimilarity between EEG recordings through a non-linear dynamical system approach.

A new measure of dissimilarity between two EEG segments is proposed. It is derived from the application of the mathematical concept of distance between series of one-step predictions according to the estimated non-linear autoregressive functions. The non-linear autoregressive estimation is performed by non-parametric regression using kernel estimators.

Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression.

A spatial-temporal model for the description of electroencephalographic (EEG) data is introduced that combines smooth reconstruction in the spatial domain and autoregressive representation in the time domain. Its spatial aspect is formulated in a general framework that covers interpolation, smoothing, and regression. Contrary to the multivariate time series models used for EEG analysis up to date, the introduced model provides a smooth spatial reconstruction of the EEG cross-spectrum, keeping the condition of nonnegative definiteness.

A global scale factor in brain topography.

In this paper statistical resampling techniques are used to support the presence of an equiangular first principal component in EEG log spectra and therefore the existence of a global scale factor in EEG recordings. the Log transformed spectra from a normal sample (n = 211, age 5-97) were analyzed. To reach this conclusion a method for estimating the scale factor is introduced.

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.

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.