Eficacia y seguridad de la melatonina para el manejo de los trastornos del sueño en el autismo: meta-análisis

INTRODUCCIÓN: El 80% de los niños y adolescentes con trastornos del espectro autista (TEA) presenta algún trastorno del sueño, en cuya génesis al parecer intervienen alteraciones en la regulación de la melatonina. El objetivo de este metaanálisis fue determinar la eficacia y seguridad de la melatonina para el manejo de ciertos trastornos del sueño en niños con TEA. MÉTODOS: Tres revisores extrajeron los datos relevantes de los ensayos clínicos aleatorizados doble ciego de alta calidad publicados en bases de datos primarias, de ensayos clínicos, de revisiones sistemáticas y de literatura gris; además se realizó búsqueda en bola de nieve. Se analizaron los datos con RevMan 5.3. Se realizó un análisis del inverso de la varianza por un modelo de efectos aleatorios para las diferencias de medias de los desenlaces propuestos: duración del tiempo total, latencia de sueño y número de despertares nocturnos. Se evaluó la heterogeneidad interestudios con el parámetro I2 RESULTADOS: La búsqueda inicial arrojó 355 resultados, de los cuales tres cumplieron los criterios de selección. La melatonina resultó ser un medicamento seguro y eficaz para aumentar la duración total del sueño y disminuir la latencia de sueño en niños y adolescentes con TEA; hasta el momento la evidencia sobre el número de despertares nocturnos no es estadísticamente significativa. DISCUSIÓN: A la luz de la evidencia disponible, la melatonina es una elección segura y eficaz para el manejo de ciertos problemas del sueño en niños y adolescentes con TEA. Es necesario realizar estudios con mayores tamaños muestrales y comparados con otros medicamentos disponibles en el mercado.

Biorretroalimentación de frecuencia cardíaca durante el ejercicio físico : resultados e implicaciones para la investigación futura.

Resumen tomado de la revista

Cartografía cerebral y TDAH.

Se propone investigar la utilidad de la técnica EEG cuantificada o QEEG para el estudio y diagnóstico del TDAH (Trastono de Déficit de Atención e Hiperactividad). En particular, se estudia la capacidad para diferenciar población con TDAH de sujetos de controles sin diagnóstico y con otros diagnósticos psicopatológicos. La muestra esta compuesta por niños y niñas de cuatro a diecisiete años remitidos para estudio a la Unidad de Salud Mental-Infanto Juvenil del Servicio de Psiquiatría de Burgos. Se forman tres grupos experimentales, de al menos cuarenta niños y niñas cada uno, un grupo de control, otro con TDAH y el último con otros diagnósticos psicopatológicos. Cada sujeto es valorado de modo independiente en los servicios de Neurofisiología y Psiquiatría del Complejo Hospitalario Universitario de Burgos. Cada diagnóstico se establece por procedimientos clínicos y psicométricos usuales en la unidad de Salud Mental Infanto-Juvenil. Para concluir lo expuesto en el proyecto de investigación, se deja abierta la investigación, para continuar con un posterior análisis de la técnica EGG cuantificada o QEEG para el estudio y diagnóstico del TDAH.

Manifestaciones clínicas de las epilepsias : epilepsias infantiles.

Conocer las epilépsias desde la ciencia médica ya que ésta, ha hecho importantes avances en el tratamiento satisfactorio, por medio de fármacos para la disminución y en algunos casos, incluso la eliminación de los ataques. Conocer pues a través del aspecto médico qué es una epilepsia, la clasificación clínica y electroencefalográfica de las crisis epilépticas, su etiología y las diversas manifestaciones psiquiátricas, posteriormente ver el tratamiento psicopedagógico del niño epiléptico viendo posibles técnicas generales de tratamiento psicopedagógico para el niño epiléptico. En cuanto a técnicas desde el punto de vista clínico: están las medidas higiénico dietéticas y tratamiento farmacológico basándose en antiepilépticos (fenitoina, carbamacepina, ácido valproica, fenobarbital, primidona, benzodiacepina, etosuximida). En cuanto al tratamiento psicopedagógico del niño epileptico con una gran ayuda que aporta la actividad del profesor. La epilepsia puede ser definida como un trastorno paroxístico recurrente de la función cerebral caracterizado por ataques súbitos breves de conciencia alterada, actividad motora, fenómenos sensoriales o conducta inapropiada. Puede ser clasificada como sintomática o idiopática. La edad de comienzo en la epilepsia idiopática se fija entre los dos y los catorce años y suelen relacionarse con defectos del desarrollo, lesiones durante el nacimiento o una enfermedad metabólica que afecte al cerebro, las que comienzan después de los veinticinco años de edad, suelen ser sintomáticas, y secundarias a traumatismos cerebrales tumores u otra enfermedad cerebral orgánica. Sin embargo las enfermedades focales del cerebro pueden causar epilepsia a cualquier edad. Los ataques convulsivos pueden asociarse a diversos trastornos cerebrales o generales, como resultado de una alteración focal o generalizada de la función cortical, y se puede demostrar generalmente por EGG. Ninguna etiología única tiene un apoyo definitivo. La historia y la exploración neurológica dan una información rápida y exacta, sobre el estado del cerebro. Otros estudios complementarios deben incluir una historia familiar detallada, una exploración física general y un estado neurológico. Igualmente son necesarios análisis de glucosa y calcio en suero, radiología de craneo y EEG. La terapeútica farmacológica puede controlar los ataques de gran mal en un cincuenta por ciento de los casos y reducir mucho la frecuencia de los ataques en otro treinta y cinco por ciento, controlar los ataques de petit mal en un treinta y tres por ciento controlar los ataques psicomotores en un veintiocho por ciento y reducir la frecuencia en un cincuenta por ciento. El pronóstico del epiléptico puede ser variable.

The relation between the electroencephalogram and the ease of autonomic conditioning

This paper discusses the results of a study to determine a relationship between the EEG pattern and autonomic conditioning.

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced.

The human thalamus processes syntactic and semantic language violations

Numerous linguistic operations have been assigned to cortical brain areas, but the contributions of subcortical structures to human language processing are still being discussed. Using simultaneous EEG recordings directly from deep brain structures and the scalp, we show that the human thalamus systematically reacts to syntactic and semantic parameters of auditorily presented language in a temporally interleaved manner in coordination with cortical regions. In contrast, two key structures of the basal ganglia, the globus pallidus internus and the subthalamic nucleus, were not found to be engaged in these processes. We therefore propose that syntactic and semantic language analysis is primarily realized within cortico-thalamic networks, whereas a cohesive basal ganglia network is not involved in these essential operations of language analysis.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.

Towards natural human computer interaction in BCI

BCI systems require correct classification of signals interpreted from the brain for useful operation. To this end this paper investigates a method proposed in [1] to correctly classify a series of images presented to a group of subjects in [2]. We show that it is possible to use the proposed methods to correctly recognise the original stimuli presented to a subject from analysis of their EEG. Additionally we use a verification set to show that the trained classification method can be applied to a different set of data. We go on to investigate the issue of invariance in EEG signals. That is, the brain representation of similar stimuli is recognisable across different subjects. Finally we consider the usefulness of the methods investigated towards an improved BCI system and discuss how it could potentially lead to great improvements in the ease of use for the end user by offering an alternative, more intuitive control based mode of operation.

Toward construction of an inexpensive brain computer interface for goal oriented applications

The paper describes the implementation of an offline, low-cost Brain Computer Interface (BCI) alternative to more expensive commercial models. Using inexpensive general purpose clinical EEG acquisition hardware (Truscan32, Deymed Diagnostic) as the base unit, a synchronisation module was constructed to allow the EEG hardware to be operated precisely in time to allow for recording of automatically time stamped EEG signals. The synchronising module allows the EEG recordings to be aligned in stimulus time locked fashion for further processing by the classifier to establish the class of the stimulus, sample by sample. This allows for the acquisition of signals from the subject’s brain for the goal oriented BCI application based on the oddball paradigm. An appropriate graphical user interface (GUI) was constructed and implemented as the method to elicit the required responses (in this case Event Related Potentials or ERPs) from the subject.

Single-trial event-related potential analysis for brain-computer interfaces

Abstract. Different types of mental activity are utilised as an input in Brain-Computer Interface (BCI) systems. One such activity type is based on Event-Related Potentials (ERPs). The characteristics of ERPs are not visible in single-trials, thus averaging over a number of trials is necessary before the signals become usable. An improvement in ERP-based BCI operation and system usability could be obtained if the use of single-trial ERP data was possible. The method of Independent Component Analysis (ICA) can be utilised to separate single-trial recordings of ERP data into components that correspond to ERP characteristics, background electroencephalogram (EEG) activity and other components with non- cerebral origin. Choice of specific components and their use to reconstruct “denoised” single-trial data could improve the signal quality, thus allowing the successful use of single-trial data without the need for averaging. This paper assesses single-trial ERP signals reconstructed using a selection of estimated components from the application of ICA on the raw ERP data. Signal improvement is measured using Contrast-To-Noise measures. It was found that such analysis improves the signal quality in all single-trials.

Rejoinder to 'Performance of different synchronisation measures in real data: A case study on electroencephalograhic signals

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga et al. [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga et al. in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga et al. in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Comment on "Performance of different synchronization measures in real data: A case study on electroencephalographic signals"

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

Single tap identification for fast BCI control

One of the major aims of BCI research is devoted to achieving faster and more efficient control of external devices. The identification of individual tap events in a motor imagery BCI is therefore a desirable goal. EEG is recorded from subjects performing and imagining finger taps with their left and right hands. A Differential Evolution based feature selection wrapper is used in order to identify optimal features in the spatial and frequency domains for tap identification. Channel-frequency band combinations are found which allow differentiation of tap vs. no-tap control conditions for executed and imagined taps. Left vs. right hand taps may also be differentiated with features found in this manner. A sliding time window is then used to accurately identify individual taps in the executed tap and imagined tap conditions. Highly statistically significant classification accuracies are achieved with time windows of 0.5 s and more allowing taps to be identified on a single trial basis.