Publications by authors named "Eduardo Aubert Vazquez"

The electroencephalogram (EEG) is a fundamental diagnostic procedure that explores brain function. This manuscript describes the characteristics of a sample of healthy at-term infants. One hundred and three (103) infants from Mexico between 15 days and 12.

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This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance.

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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.

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The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18-68 years).

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Article Synopsis
  • The Tomographic Quantitative Electroencephalography (qEEGt) toolbox, integrated with the MNI Neuroinformatics Ecosystem, allows users to create age-corrected EEG normative Statistical Parametric Maps based on a normative database.
  • Developed at the Cuban Neuroscience Center as part of the CHBMP, this validated toolbox offers features like EEG scalp spectra calculation and source spectra estimation using Variable Resolution Electrical Tomography (VARETA).
  • The open-source release on GitHub and Zenodo, along with user-friendly visualization tools, aims to promote standardized qEEGt methods for research and clinical use, marking the first phase of the CCC neuroinformatic project.
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The curtain of technical limitations impeding rat multichannel non-invasive electroencephalography (EEG) has risen. Given the importance of this preclinical model, development and validation of EEG source imaging (ESI) is essential. We investigate the validity of well-known human ESI methodologies in rats which individual tissue geometries have been approximated by those extracted from an MRI template, leading also to imprecision in electrode localizations.

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This paper extends previously developed 3D SPM for Electrophysiological Source Imaging (Bosch et al., 2001) for neonate EEG. It builds on a prior paper by our group that established age dependent means and standard deviations for the scalp EEG Broad Band Spectral Parameters of children in the first year of life.

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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.

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In this paper, the Bayesian Theory is used to formulate the Inverse Problem (IP) of the EEG/MEG. This formulation offers a comparison framework for the wide range of inverse methods available and allows us to address the problem of model uncertainty that arises when dealing with different solutions for a single data. In this case, each model is defined by the set of assumptions of the inverse method used, as well as by the functional dependence between the data and the Primary Current Density (PCD) inside the brain.

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