Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study.

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer's disease and mild cognitive impairment.

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network's attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

Aplicación de una estrategia metodológica para desarrollar habilidades lingüísticas en idioma inglés / Implementing a methodological strategy to develop language skills in the English language

Introducción: resulta imprescindible aprender el idioma inglés pues permite la comunicación con personas de otros países, se enfatiza en el ámbito académico universitario en el perfeccionamiento de los métodos y procedimientos en las clases donde se imparte como un segundo idioma.Objetivo: introducir un vocabulario técnico desde el primer año de la carrera para elevar el nivel de satisfacción y mejorar la motivación por el inglés.Métodos: se utilizó el método de análisis y síntesis, análisis documental para llegar a conclusiones sobre la literatura consultada sobre estrategias metodológicas, inglés para fines específicos, habilidades lingüísticas y métodos comunicativos. Se aplicó una encuesta a estudiantes y profesores de inglés para conocer su opinión acerca de la estrategia y entrevistas a docentes de experiencia en Educación Médica para conocer su opinión.Resultados: las entrevistas a docentes de experiencia en Educación Médica y profesores de inglés de la República de Angola plantearon que la estrategia es adecuada e importante para el desarrollo de las habilidades lingüísticas. Además las encuestas a los estudiantes de primero a cuarto año para conocer el nivel de aceptación del trabajo con vocabulario técnico de medicina y situaciones relacionadas con escenarios de salud mostraron resultados satisfactorios. Conclusiones: se elaboró una estrategia metodológica para desarrollar habilidades lingüísticas en idioma inglés en estudiantes de la Facultad de Medicina de Cabinda. Ellos muestran un alto nivel de satisfacción con el uso del vocabulario técnico y situaciones comunicativas relacionadas con su carrera lo cual eleva el nivel de motivación de los estudiantes por el estudio del inglés como idioma extranjero(AU)

Introduction: it is essential to learn the English language since it allows communication with people from other countries; it is emphasized in the university academic environment in perfecting methods and procedures in classes where it is taught as a second language.Objective: to introduce a technical vocabulary from the first year of the career to raise the level of satisfaction and motivation and to improving English.Methods: it was used the method of analysis and synthesis, documentary analysis in order to reach conclusions concerning the consulted literature on methodological strategies, English for Specific Purposes, linguistic skills and communicative methods. A survey was applied to students and English teachers to know their opinion about the strategy and experienced teachers in Medical Education were interviewed to get their opinion on the matter.Results: interviews with experienced teachers in Medical Education and English teachers of the Republic of Angola considered the strategy to be appropriate and important for the development of language skills. Besides the surveys applied to the students from first to fourth year to determine the level of acceptance of the work with technical medical terms and situations related to health scenarios showed satisfactory results.Conclusions: a methodological strategy was drawn up to develop English language skills in students of Cabinda's Faculty of Medicine. They show a high level of satisfaction with the use of technical vocabulary and communicative situations related to their career thus raising the level of student motivation in studying English as a foreign language (AU)

Studying the topological organization of the cerebral blood flow fluctuations in resting state.

In this paper the cerebral blood flow (CBF) in resting state obtained from SPECT imaging is employed as a hemodynamics descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in CBF between pairs of regions was measured by computing the Pearson correlation coefficient across 31 normal subjects. We demonstrated the CBF connectivity matrices follow 'small-world' attributes similar to previous studies using different modalities of neuroimaging data (MRI, fMRI, EEG, MEG). The highest concurrent fluctuations in CBF were detected between homologous cortical regions (homologous callosal connections). It was found that the existence of structural core regions or hubs positioned on a high proportion of shortest paths within the CBF network. These were anatomically distributed in frontal, limbic, occipital and parietal regions that suggest its important role in functional integration. Our findings point to a new possibility of using CBF variable to investigate the brain networks based on graph theory in normal and pathological states. Likewise, it opens a window to future studies to link covariation between morphometric descriptors, axonal connectivity and CBF processes with a potential diagnosis applications.

Aplicación de una estrategia metodológica para desarrollar habilidades lingüísticas en idioma inglés / Implementing a methodological strategy to develop language skills in the English language

Introducción: resulta imprescindible aprender el idioma inglés pues permite la comunicación con personas de otros países, se enfatiza en el ámbito académico universitario en el perfeccionamiento de los métodos y procedimientos en las clases donde se imparte como un segundo idioma.Objetivo: introducir un vocabulario técnico desde el primer año de la carrera para elevar el nivel de satisfacción y mejorar la motivación por el inglés.Métodos: se utilizó el método de análisis y síntesis, análisis documental para llegar a conclusiones sobre la literatura consultada sobre estrategias metodológicas, inglés para fines específicos, habilidades lingüísticas y métodos comunicativos. Se aplicó una encuesta a estudiantes y profesores de inglés para conocer su opinión acerca de la estrategia y entrevistas a docentes de experiencia en Educación Médica para conocer su opinión.Resultados: las entrevistas a docentes de experiencia en Educación Médica y profesores de inglés de la República de Angola plantearon que la estrategia es adecuada e importante para el desarrollo de las habilidades lingüísticas. Además las encuestas a los estudiantes de primero a cuarto año para conocer el nivel de aceptación del trabajo con vocabulario técnico de medicina y situaciones relacionadas con escenarios de salud mostraron resultados satisfactorios. Conclusiones: se elaboró una estrategia metodológica para desarrollar habilidades lingüísticas en idioma inglés en estudiantes de la Facultad de Medicina de Cabinda. Ellos muestran un alto nivel de satisfacción con el uso del vocabulario técnico y situaciones comunicativas relacionadas con su carrera lo cual eleva el nivel de motivación de los estudiantes por el estudio del inglés como idioma extranjero

Introduction: it is essential to learn the English language since it allows communication with people from other countries; it is emphasized in the university academic environment in perfecting methods and procedures in classes where it is taught as a second language.Objective: to introduce a technical vocabulary from the first year of the career to raise the level of satisfaction and motivation and to improving English.Methods: it was used the method of analysis and synthesis, documentary analysis in order to reach conclusions concerning the consulted literature on methodological strategies, English for Specific Purposes, linguistic skills and communicative methods. A survey was applied to students and English teachers to know their opinion about the strategy and experienced teachers in Medical Education were interviewed to get their opinion on the matter.Results: interviews with experienced teachers in Medical Education and English teachers of the Republic of Angola considered the strategy to be appropriate and important for the development of language skills. Besides the surveys applied to the students from first to fourth year to determine the level of acceptance of the work with technical medical terms and situations related to health scenarios showed satisfactory results.Conclusions: a methodological strategy was drawn up to develop English language skills in students of Cabinda's Faculty of Medicine. They show a high level of satisfaction with the use of technical vocabulary and communicative situations related to their career thus raising the level of student motivation in studying English as a foreign language

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.

Automated discrimination of brain pathological state attending to complex structural brain network properties: the shiverer mutant mouse case.

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbp(shi)/Mbp(shi), n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6-100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.

Covert face recognition without the fusiform-temporal pathways.

Patients with prosopagnosia are unable to recognize faces consciously, but when tested indirectly they can reveal residual identification abilities. The neural circuitry underlying this covert recognition is still unknown. One candidate for this function is the partial survival of a pathway linking the fusiform face area (FFA) and anterior-inferior temporal (AIT) cortex, which has been shown to be essential for conscious face identification. Here we performed functional magnetic, and diffusion tensor imaging in FE, a patient with severe prosopagnosia, with the goal of identifying the neural substrates of his robust covert face recognition. FE presented massive bilateral lesions in the fusiform gyri that eliminated both FFAs, and also disrupted the fibers within the inferior longitudinal fasciculi that link the visual areas with the AITs and medial temporal lobes. Therefore participation of the fusiform-temporal pathway in his covert recognition was precluded. However, face-selective activations were found bilaterally in his occipital gyri and in his extended face system (posterior cingulate and orbitofrontal areas), the latter with larger responses for previously-known faces than for faces of strangers. In the right hemisphere, these surviving face selective-areas were connected via a partially persevered inferior fronto-occipital fasciculus. This suggests an alternative occipito-frontal pathway, absent from current models of face processing, that could explain the patient's covert recognition while also playing a role in unconscious processing during normal cognition.

Brain hemispheric structural efficiency and interconnectivity rightward asymmetry in human and nonhuman primates.

Evidence for interregional structural asymmetries has been previously reported for brain anatomic regions supporting well-described functional lateralization. Here, we aimed to investigate whether the two brain hemispheres demonstrate dissimilar general structural attributes implying different principles on information flow management. Common left hemisphere/right hemisphere structural network properties are estimated and compared for right-handed healthy human subjects and a nonhuman primate, by means of 3 different diffusion-weighted magnetic resonance imaging fiber tractography algorithms and a graph theory framework. In both the human and the nonhuman primate, the data support the conclusion that, in terms of the graph framework, the right hemisphere is significantly more efficient and interconnected than the left hemisphere, whereas the left hemisphere presents more central or indispensable regions for the whole-brain structural network than the right hemisphere. From our point of view, in terms of functional principles, this pattern could be related with the fact that the left hemisphere has a leading role for highly demanding specific process, such as language and motor actions, which may require dedicated specialized networks, whereas the right hemisphere has a leading role for more general process, such as integration tasks, which may require a more general level of interconnection.

Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks.

Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.

Mathematical description of q-space in spherical coordinates: exact q-ball imaging.

Novel methodologies have been recently developed to characterize the microgeometry of neural tissues and porous structures via diffusion MRI data. In line with these previous works, this article provides a detailed mathematical description of q-space in spherical coordinates that helps to highlight the differences and similarities between various related q-space methodologies proposed to date such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and diffusion orientation transform imaging (DOT). This formulation provides a direct relationship between the orientation distribution function (ODF) and the diffusion data without using any approximation. Under this relationship, the exact ODF can be computed by means of the Radon transform of the radial projection (in q-space) of the diffusion MRI signal. This new methodology, termed exact q-ball imaging (EQBI), was put into practice using an analytical ODF estimation in terms of spherical harmonics that allows obtaining model-free and model-based reconstructions. This work provides a new framework for combining information coming from diffusion data recorded on multiple spherical shells in q-space (hybrid diffusion imaging encoding scheme), which is capable of mapping ODF to a high accuracy. This represents a step toward a more efficient development of diffusion MRI experiments for obtaining better ODF estimates.

Inferring multiple maxima in intravoxel white matter fiber distribution.

A new methodology is introduced that characterizes the intravoxel orientation distribution function (ODF) based on a single-fiber model of the diffusion MRI signal. Using a Bayesian framework the probability of finding a fiber in a specific orientation is obtained. The proposed ODF estimation relies on a cigar-like diffusion tensor model, the methodology is thus denominated Bayesian cigar-like diffusion tensor (BCDT). This work makes two major contributions: 1) the study of single-fiber models in detecting fibers with different volume fractions in a voxel, and 2) the introduction of the Nth-root correction to improve the detection of fibers with smaller volume fractions, where N is the number of diffusion MRI measurements. It is demonstrated that the incomplete signal modeling fails to reconstruct the relative fiber volume fractions, especially when the intravoxel diffusion profiles have dissimilar contributions to the diffusion MRI signal. In this situation the fibers with smaller contributions are hardly detectable. The BCDT method proposed here reduces this effect by introducing the Nth-root correction, making multiple fibers estimable. The performance of the new methodology is illustrated using synthetic and real data, as well as the data from a phantom of intersecting capillaries.

A Bayesian framework to identify principal intravoxel diffusion profiles based on diffusion-weighted MR imaging.

In this paper we introduce a new method to characterize the intravoxel anisotropy based on diffusion-weighted imaging (DWI). The proposed solution, under a fully Bayesian formalism, deals with the problem of joint Bayesian Model selection and parameter estimation to reconstruct the principal diffusion profiles or primary fiber orientations in a voxel. We develop an efficient stochastic algorithm based on the reversible jump Markov chain Monte Carlo (RJMCMC) method in order to perform the Bayesian computation. RJMCMC is a good choice for this problem because of its ability to jump between models of different dimensionality. This methodology provides posterior estimates of the parameters of interest (fiber orientation, diffusivities etc) unconditional of the model assumed. It also gives an empirical posterior distribution of the number of primary nerve fiber orientations given the DWI data. Different probability maps can be assessed using this methodology: 1) the intravoxel fiber orientation map (or orientational distribution function) that gives the probability of finding a fiber in a particular spatial orientation; 2) a three-dimensional map of the probability of finding a particular number of fibers in each voxel; 3) a three-dimensional MaxPro (maximum probability) map that provides the most probable number of fibers for each voxel. In order to study the performance and reliability of the presented approach, we tested it on synthetic data; an ex-vivo phantom of intersecting capillaries; and DWI data from a human subject.

Studying the human brain anatomical network via diffusion-weighted MRI and Graph Theory.

Our goal is to study the human brain anatomical network. For this, the anatomical connection probabilities (ACP) between 90 cortical and subcortical brain gray matter areas are estimated from diffusion-weighted Magnetic Resonance Imaging (DW-MRI) techniques. The ACP between any two areas gives the probability that those areas are connected at least by a single nervous fiber. Then, the brain is modeled as a non-directed weighted graph with continuous arc weights given by the ACP matrix. Based on this approach, complex networks properties such as small-world attributes, efficiency, degree distribution, vulnerability, betweenness centrality and motifs composition are studied. The analysis was carried out for 20 right-handed healthy subjects (mean age: 31.10, S.D.: 7.43). According to the results, all networks have small-world and broad-scale characteristics. Additionally, human brain anatomical networks present bigger local efficiency and smaller global efficiency than the corresponding random networks. In a vulnerability and betweenness centrality analysis, the most indispensable and critical anatomical areas were identified: putamens, precuneus, insulas, superior parietals and superior frontals. Interestingly, some areas have a negative vulnerability (e.g. superior temporal poles, pallidums, supramarginals and hechls), which suggest that even at the cost of losing in global anatomical efficiency, these structures were maintained through the evolutionary processes due to their important functions. Finally, symmetrical characteristic building blocks (motifs) of size 3 and 4 were calculated, obtaining that motifs of size 4 are the expanded version of motif of size 3. These results are in agreement with previous anatomical studies in the cat and macaque cerebral cortex.

Estimating brain functional connectivity with sparse multivariate autoregression.

There is much current interest in identifying the anatomical and functional circuits that are the basis of the brain's computations, with hope that functional neuroimaging techniques will allow the in vivo study of these neural processes through the statistical analysis of the time-series they produce. Ideally, the use of techniques such as multivariate autoregressive (MAR) modelling should allow the identification of effective connectivity by combining graphical modelling methods with the concept of Granger causality. Unfortunately, current time-series methods perform well only for the case that the length of the time-series Nt is much larger than p, the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging for which relatively short time-series are measured over thousands of voxels. Methods are introduced for dealing with this situation by using sparse MAR models. These can be estimated in a two-stage process involving (i) penalized regression and (ii) pruning of unlikely connections by means of the local false discovery rate developed by Efron. Extensive simulations were performed with idealized cortical networks having small world topologies and stable dynamics. These show that the detection efficiency of connections of the proposed procedure is quite high. Application of the method to real data was illustrated by the identification of neural circuitry related to emotional processing as measured by BOLD.

Estimating brain functional connectivity with sparse multivariate autoregression / s. t

There is much current interest in identifying the anatomical and functional circuits that are the basis of the brain's computations, with hope that functional neuroimaging techniques will allow the in vivo study of these neural processes through the statistical analysis of the time-series they produce. Ideally, the use of techniques such as multivariate autoregressive (MAR) modelling should allow the identification of effective connectivity by combining graphical modelling methods with the concept of Granger causality. Unfortunately, current time-series methods perform well only for the case that the length of the time-series Nt is much larger than p, the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging for which relatively short time-series are measured over thousands of voxels. Methods are introduced for dealing with this situation by using sparse MAR models. These can be estimated in a two-stage process involving (i) penalized regression and (ii) pruning of unlikely connections by means of the local false discovery rate developed by Efron. Extensive simulations were performed with idealized cortical networks having small world topologies and stable dynamics. These show that the detection efficiency of connections of the proposed procedure is quite high. Application of the method to real data was illustrated by the identification of neural circuitry related to emotional processing as measured by BOLD(AU)